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Generative Artificial Intelligence (GenAI) offers dynamic approaches for environmental assessment and sustainability. This review explores GenAI

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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.