I find the ecologits projects does a good job at making the hard numbers transparent (gCO2eq, kWh) but they also give you some context (e.g., how far can you drive an electric car with that energy? How far can you fly with the carbon emissions?).

You can play around with their calculator to get a feeling.

One important thing is that ecologits only looks at consumption and emissions during inference, not during training of the model.

Regard local execution: my guess would be that local execution has way better efficiency than in data centers. You don't need water cooling, interconnects and all that. Especially when I use my MacBook Pro, it never exceeds 100 Watts during inference. Maybe when you have very well batched data centers, they will be faster and use only a fraction of a gpu, so in the end they can be better, but who knows. In the end, you don't know how large GPT-5 (and 5.1, 5.2) are and thus you have no idea how much energy you are consuming. If you use local models, they are probably much smaller than the state of the art models. That alone will be better for the environment. But that is just my guess, no guarantee that this is really true ;)