Environment

During the intense 12-day conflict between Israel and Iran in June 2025, Azerbaijan emerged not as a neutral observer, but as a silent enabler—allegedly providing key logistical and intelligence support to Israel and, by extension, the United States. Under President Ilham aliyev, Azerbaijan has steadily deepened military and technological ties with Israel, positioning itself as a quiet but crucial player in the regional power struggle.

Recent disclosures, allegedly originating from cyber intrusions conducted by Iranian hacking units targeting Israeli nuclear affairs, have brought to light a highly sensitive and potentially destabilizing claim. According to these documents, Rafael Grossi, Director General of the International Atomic Energy Agency (IAEA), is accused of clandestinely transferring classified Iranian nuclear information to Israeli officials over an extended period, purportedly under the pretext of building Iranian trust.

AI-driven simulations have transformative potential for understanding complex biological processes and ecological systems. Here’s how they contribute to these fields:

Machine learning algorithms can significantly enhance climate modeling and predictions in several ways:

AI has become a powerful tool in genomics, transforming how we analyze and interpret large-scale genomic data. Here are some key ways AI contributes to this field:

Pattern Recognition
AI algorithms, particularly deep learning models, excel at identifying patterns within large datasets. In genomics, this means AI can detect complex patterns in DNA sequences that might be missed by traditional methods. For example, AI can help identify genetic variants associated with diseases by recognizing patterns in gene expression data.

Raising public awareness about the environmental impacts of AI and influencing the sustainability of AI development involves a multi-faceted approach:

Raising Public Awareness

Education and Outreach: 

To effectively manage and minimize the environmental impacts of AI, a comprehensive regulatory framework is needed. Here are some key regulatory measures that could be implemented:

Energy Efficiency Standards: Establish standards for the energy efficiency of AI systems. This includes setting requirements for hardware and algorithms to minimize energy consumption and encourage the use of energy-efficient computing resources.

Conducting a comprehensive Life Cycle Assessment (LCA) of AI systems involves analyzing the environmental impacts across all stages of their life cycle, from raw material extraction to disposal. Here are key methodologies and approaches to consider:

AI can be a powerful tool in water management and conservation, offering solutions across various aspects of the field. Here are some ways AI can be leveraged to enhance these efforts:

The environmental impacts of AI raise several ethical considerations that require careful attention and action. Here’s a breakdown of the key issues and potential approaches to addressing them:

Energy Consumption and Carbon Footprint

• Issue: Training and running large AI models, particularly deep learning models, consume substantial amounts of computational power, which translates to significant energy usage and carbon emissions.

Ethical Considerations: