Generative Artificial Intelligence for Environmental Assessment: A New Paradigm for Sustainability Analysis

Area of Science:

• Environmental Science and Sustainability
• Artificial Intelligence and Machine Learning
• Environmental Management

Background:

• Traditional environmental assessment models are often static and limited.
• Emerging Generative Artificial Intelligence (GenAI) presents a new paradigm for dynamic and participatory environmental management.
• GenAI integrates data synthesis, scenario modeling, and governance insights.

Purpose of the Study:

• To comprehensively review the emerging role of GenAI in environmental assessment and sustainability analysis.
• To identify and analyze key GenAI architectures and their applications in environmental contexts.
• To propose a framework for responsible GenAI implementation in environmental management.

Main Methods:

• Systematic Literature Review (SLR) using the CIMO framework.
• Analysis of 182 scholarly and technical publications (2015-2025).
• Synthesis of developments in Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Large Language Models (LLMs), and Diffusion Models.

Main Results:

• GenAI shows potential in addressing data scarcity, enhancing model scalability, and promoting participatory decision-making.
• Key applications include synthetic data generation, scenario simulation, remote sensing, predictive analytics, and public engagement.
• Challenges identified include interpretability, data bias, validation, environmental footprint, and ethical governance.

Conclusions:

• GenAI can significantly advance environmental assessment and sustainability analysis.
• Responsible integration requires a three-tier framework focusing on technical fidelity, transparency, and governance.
• Effective implementation necessitates hybrid modeling, participatory data governance, and sustainable AI infrastructures for transparency, accountability, and equity.