Warning: Wonky 🧵 on vague energy/climate modeling ideas.
Many papers I read in this field aim to compare climate mitigation scenarios or optimize mitigation pathways.
The modeled transitions have lots of impacts: ⬆️ or ⬇️ deaths, illnesses, jobs, money, and so on.
It's hard to compare or optimize pathways when there are so many different, and often competing, goals.
One pathway might cost less but sicken and kill more people. Another might cost a lot but also create a ton of jobs. And so on.
The conversion process involves assigning prices to the various non-monetary objectives. Creating 1 job is worth $X, avoiding 1 year of serious illness for 1 person is worth $Y, and so on.
They usually value 1 human life at about $12 million.
This is a very common move, but it's sorta bonkers. "How many job creations is a human life worth?" is just a weird-ass question to ask, yet researchers implicitly answer it all the time. The answers are of course totally subjective, but rarely discussed in much depth.
Another approach is to rank the competing objectives by importance, then compare or optimize pathways sequentially, from most to least important.
For example: First save as many lives as possible, then avoid as many person-years of illness as possible, then save as much money as possible.
This isn't my primary research area - I mostly develop software to better operate heating/cooling equipment, EVs, batteries, etc. - so I'm not as familiar with the literature as many others are. But I've seen very few papers that take this alternative, "rank then sequentially optimize" approach.
i'm curious what models you're using, if you have a moment, i've had some experience with GCAM and other IAMs but i'm wondering what else is out there, the topic is proximate to my field but i mainly do other things
