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Kore University of Enna
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Prof. Dr. Gabriela Topa
Social and organizational Psychology, Universidad Nacional de Educacion a Distancia
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Home > Archives > Vol. 10 No. 9 (2025): Published > Research Articles
ESP-3983

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2025-10-16

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Vol. 10 No. 9 (2025): Published

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Research Articles

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Copyright (c) 2025 Muzaffer Yassen, Marwan Salah Noaman, Taghreed Alaa Mohammed Ali Hassan, Nameer Hashim Qasim, Intesar Abbas, Yurii Khlaponin

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Muzaffer Yassen, Marwan Salah Noaman, Taghreed Alaa Mohammed Ali Hassan, Nameer Hashim Qasim, Intesar Abbas, & Yurii Khlaponin. (2025). Big data and AI in environmental decision-making: Legal and ethical challenges. Environment and Social Psychology, 10(9), ESP-3983. https://doi.org/10.59429/esp.v10i9.3983
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Big data and AI in environmental decision-making: Legal and ethical challenges

Muzaffer Yassen

Al-Turath University, Baghdad 10013, Iraq

Marwan Salah Noaman

Al-Mansour University College, Baghdad 10067, Iraq

Taghreed Alaa Mohammed Ali Hassan

Al-Mamoon University College, Baghdad 10012, Iraq

Nameer Hashim Qasim

Al-Rafidain University College, Baghdad 10064, Iraq

Intesar Abbas

Madenat Alelem University College, Baghdad 10006, Iraq

Yurii Khlaponin

State University of Trade and Economics, 19 Kioto Avenue, Kyiv 02156, Ukraine


DOI: https://doi.org/10.59429/esp.v10i9.3983


Keywords: AI-driven environmental governance; Big Data in sustainability; machine learning in climate forecasting; bias mitigation in AI; transparency in AI decision-making; resource efficiency optimization.


Abstract

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.


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