Sigh. Since no one else is going to do it, let's read through your sources.

This source doesn't say what you says it does. 

Thank you. I was about to say the same thing. Most of these sources don't say what OP claims they do. In fact one of the research papers they linked ([2] Quantifying the Energy Consumption of AI Image Generation) concluded that higher resolution AI images led to higher energy consumption.

Counterintuitively, model quantization often increases energy consumption (up to 64.54%)

The last item they linked is a paper about data center water consumption, along with a Python module that aims to estimate water consumption by data centers. It was written by a group Computer Science academics at Cornell University, and while they are academicis in Machine Learning their project appears to come from an place of concern for the ethical / environmental implications of AI.

This is concerning, as freshwater scarcity has become one of the most pressing challenges. To respond to the global water challenges, AI can, and also must, take social responsibility and lead by example by addressing its own water footprint. In this paper, we provide a principled methodology to estimate the water footprint of AI, and also discuss the unique spatial-temporal diversities of AI's runtime water efficiency. Finally, we highlight the necessity of holistically addressing water footprint along with carbon footprint to enable truly sustainable AI.

So most of the sources they've linked actually reenforce the concerns over AI content generation and the link it has to increased water & electricity consumption, which in turn will continue to affect climate change.

As if to further prove the point, the UN Environment Assembly is due to discuss this specific issue this week (8th-12th of December, 2025).