Yes, on two points. 1) heat pumps are more than 100% efficient in most conditions. Because they are moving heat, rather than generating it, they can add more heat energy to your home than they actually consume. 2) mix of sources. As you said, even if most of your electricity comes from gas plants, that means some can or does come from renewable sources or nuclear. This makes it much easier to transition to even more renewables, since the consumer side doesn't need to change anything as gas plants are phased out. It's future planning with immediate benefits from point 1.
Point 1 can be a bit complex, since in extreme conditions air source heat pumps may rely on resistive heating which is only 100% efficient. Alternatives like ground source HPs don't have that problem, but they are suited to fewer areas.
I challenge the idea that it is even possible to plagiarise an Acknowledgements section. It's not a substantive section of the work and has no bearing on the work itself. In addition, boilerplate is not only extremely common for Acknowledgements, for a paper it's essentially required. All of the acknowledgement sections of my papers are basically identical, and are basically identical to all of my colleagues on the same funding, because that's how it works. Did we plagiarise each other or our supervisor by saying "This study was supported by ERC Horizon 2030 grant no. Xxxxxx, The Extremely Solid Study (TESS)"?
Without that article actually showing what was supposedly plagiarised in her acknowledgements, I don't buy it.
These smart meters will still work as energy meters and thermostats, it's just the connectivity and 'smart' bits that will go offline.
I'm setting up a new laptop and considering which of the (many) environment managers to use this time around. My standard has been miniconda, since a big plus for me is the ability to set and download specific python version for different projects all in one tool. I also quite like having global access to different environments (i.e. environments aren't tied to specific projects). I typically have a standard GenDataSci environment always available for initially testing things out, then if I know I'll be continuing as a single project I'll make a stand alone environment for it.
But I've also used poetry for tighter control and reproducibility when I'm actually packaging to publish on PyPI. Hatch looks interesting as well but I can't tell if it includes the ability to have separate python version installs for each environment.
What workflows and managers are people using now?