Big data and AI in environmental decision-making: Legal and ethical challenges

Artificial Intelligence (AI) and Big Data are increasingly being leveraged in environmental decision-making, offering a transformative mechanism for improving predictive capability, efficiency in resource use, and transparency of governance. This study examines how AI-based models can help improve climate forecasting, disaster mitigation, water resource management, urban planning, and agricultural oversight. Utilizing machine learning algorithms, neural networks, and optimization models, AI overcomes the limitations of traditional forecasting and decision-support systems, allowing for faster and more accurate environmental assessments. AI-based models demonstrated the most significant increase in predictive accuracy due to improved predictive accuracy through minimized predictive errors across multiple environmental domains that range between 1.5% and 39.8% improvements. AI also improves decision-making efficiency reducing response times (when implementing such strategies) by 47.5% — useful in areas such as disaster preparedness and distributing the right resources. AI also assists in sustainable environmental management, where its optimizations have created 36.7% reductions in environmental resource consumption. The article also showcases how AI can help overcome biases, especially related to equity in environmental policies, leading to fairer decision-making processes. However, data availability, algorithm transparency, energy, and regulatory compliance are still challenges. Tackling resent challenges will necessitate stronger AI governance frameworks, advanced ethical guidelines, and collaborative efforts between decision-makers, scientists, and AI researchers. The highlights of this particular study illustrate how the smart integration of AI and Big Data within environmental governance can ensure efficient and accountable decision-making processes in the future.