Carbon and Water Footprint Always is a Trade Off

It is common these days--and rightly so--to evaluate the carbon or water impact of data centers used to support artificial intelligence operations. 

It also is fair to note that every action and every product has a carbon and water impact as well, as difficult as it might be to quantify the actual footprint of each product, including all the various component parts and processes that are used to create each individual product. 

Product	Water Footprint (Liters)	Explanation
Email (sent and received)	0.04	Primarily due to the energy required to power data centers.
Blog Post (created and viewed)	0.12	Similar to email, but with slightly higher energy consumption due to the creation and storage of content.
E-commerce Transaction	0.2	Includes the energy for processing payments, shipping, and product manufacturing.
Generative AI Query	0.30 - 1.00	Depends on the complexity of the query and the size of the AI model. Larger models and more complex queries require more energy and, consequently, more water.
Shirt (cotton)	2,700	Includes water used for cotton cultivation, processing, and manufacturing.
Pair of Shoes (leather)	1,000	Primarily due to the water used in cattle ranching and leather production.
Grocery Store Tomato	180	Depends on cultivation methods, but typically includes irrigation water.
Can of Beans	150	Water used for bean cultivation, processing, and packaging.
Auto (mid-size)	100,000	Includes water used in the production of materials, components, and fuel.
Toothbrush (plastic)	50	Water used in the production of plastic and packaging.
Restaurant Hamburger	2,400	Includes water used for beef production, vegetable cultivation, and food processing.

The point is that sustainability concerns exist for every physical or digital product you can imagine. We can, and should, always try to do better. But some footprint--carbon or water--is to be expected. It is a trade off.

Product	Carbon Footprint (Grams of CO2 equivalent)	Explanation
Shirt (cotton)	27,000	Includes carbon emissions from cotton cultivation, processing, and manufacturing.
Pair of Shoes (leather)	10,000	Primarily due to carbon emissions from cattle ranching and leather production.
Grocery Store Tomato	1,800	Depends on cultivation methods, but typically includes emissions from fertilizers and transportation.
Can of Beans	1,500	Carbon emissions from bean cultivation, processing, and packaging.
Auto (mid-size)	100,000	Includes carbon emissions from fuel combustion, material production, and transportation.
Toothbrush (plastic)	500	Carbon emissions from plastic production and packaging.
Restaurant Hamburger	24,000	Includes carbon emissions from beef production, vegetable cultivation, and food processing.
Email (sent and received)	4	Primarily due to the energy required to power data centers.
Blog Post (created and viewed)	12	Similar to email, but with slightly higher energy consumption due to the creation and storage of content.
E-commerce Transaction	20	Includes the energy for processing payments, shipping, and product manufacturing.
Generative AI Query	30 - 100	Depends on the complexity of the query and the size of the AI model. Larger models and more complex queries require more energy and, consequently, more carbon emissions.

The point is the need to make prudent trade offs, balancing the benefits and costs of AI, or computing, or data ceners against some amount of carbon and water consumption. We have to do likewise in all other phases of life and business.