Ruchi Jain,
Kirti Verma,
Madhulika Shukla,
Parth Khare,
- Assistant Professor, Department of Engineering Mathematics, Gyan Ganga Institute of Technology and Sciences Jabalpur, M.P, India
- Assistant Professor, Department of Engineering Mathematics, Gyan Ganga Institute of Technology and Sciences Jabalpur, M.P, India
- Professor, Department of Engineering Mathematics, Gyan Ganga Institute of Technology and Sciences Jabalpur, M.P, India
- Assistant Professor, Department of Engineering Mathematics, Gyan Ganga Institute of Technology and Sciences Jabalpur, M.P, India
Abstract
Measuring sustainability performance has become a critical priority for enterprises facing increasing regulatory pressure, stakeholder expectations, and global sustainability challenges. Traditional assessment methods, largely based on static indicators and manual reporting, often struggle to capture the multidimensional, dynamic, and data-intensive nature of sustainability. This study explores the integration of statistical and artificial intelligence (AI) approaches to evaluate and enhance the sustainability performance of enterprises in a more robust, accurate, and scalable manner. Statistical methods such as regression analysis, multivariate analysis, factor analysis, and composite sustainability indices provide a structured framework for quantifying environmental, social, and governance (ESG) dimensions. These techniques enable enterprises to identify key performance drivers, analyze trends over time, and benchmark performance across industries. However, statistical models are often limited by assumptions of linearity, data availability, and their reduced ability to handle complex, non-linear relationships. To overcome these limitations, AI-based approaches including machine learning, neural networks, decision trees, and natural language processing are increasingly being applied to sustainability measurement. AI models can process large volumes of structured and unstructured data from sources such as financial reports, sustainability disclosures, social media, and sensor data. These methods enhance predictive accuracy, automate sustainability scoring, and uncover hidden patterns that support proactive decision-making. Moreover, AI enables real-time monitoring and scenario analysis, allowing enterprises to adapt sustainability strategies under uncertain and evolving conditions. This abstract highlights the complementary role of statistical and AI approaches in sustainability performance measurement. While statistical techniques ensure transparency, interpretability, and methodological rigor, AI methods contribute flexibility, scalability, and predictive power. The combined use of these approaches offers a comprehensive framework for assessing enterprise sustainability, supporting strategic planning, risk management, and long-term value creation. The study underscores the importance of integrating advanced analytics into sustainability management systems to drive more informed, data-driven, and sustainable business practices.
Keywords: Sustainability Performance, Artificial Intelligence, Statistical Methods, Enterprises, ESG, Machine Learning, Data Analytics.
[This article belongs to Research & Reviews : Journal of Statistics ]
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Research & Reviews : Journal of Statistics
| Volume | 15 | |
| Issue | 01 | |
| Received | 07/04/2026 | |
| Accepted | 17/04/2026 | |
| Published | 30/04/2026 | |
| Publication Time | 23 Days |