2024 might have been a global breakout year for geothermal energy. Next generation technologies make it possible to drill for heat in places where this was previously impossible.

Thank you Karthik, this looks really interesting!

Hi, I did this program last year and I really enjoyed it. If anyone is considering applying but wants to ask some questions to a former participant to check whether it's the right fit for them, feel free to send me an email or a forum message!

You're right to point out the trade-off between low-carbon production and high welfare standards! Theoretically, it should be possible to adapt the model to weigh the animal welfare impacts on the extensive margin (=number of animals) by the intensity of the suffering of different animals, and to make intensity of suffering in turn a function of the carbon tax. That makes the whole model a bit more wonky but I think it's important work that needs to be done.

I would say taxing the negative effects on farmed animals based on the time they spend in pain, as assessed by the Welfare Footprint Project (WFP), is robustly beneficial.

I agree! I think it will be quite difficult to implement this, though. Will policymakers like to implement a tax based on 'suffering units' with quite some uncertainty? I wonder if we can find a decent proxy that is easier for taxation.

Thank you for the kind words and the useful feedback!

• My understanding is that calibration is picking numbers for parameters in a otherwise pre-defined model to make it match the real world, and what you're describing I think of as estimation rather than calibration. I'll add a footnote to make my use of the word a little clearer!
• You're right on the validity of the Bouyssou et al. (2024) paper. There are quite some differences in the measured elasticity estimates in the literature, unfortunately. I'm more worried about the own-price elasticity of supply, though, as the literature is much more scarce and old. 

I think one factor that adds uncertainty to climate giving is that effective climate action requires systems change and innovation breakthroughs, and that you can't simply put a price tag on these two because advocacy and innovation can fail. A more bednet-style approach to climate giving, such as forest protection and reforestation, seems promising because it offers high certainty and hardly any political opposition. Even then, these direct interventions have all kinds of uncertainty related to permanence (will the forest be cut down again?), additionality (would the forest have regrown naturally?), and leakage (will people just cut down different trees?). Unlike in global health and development, directly tackling emissions in one place can increase it somewhere else.

Personally, I've come to terms with the idea that climate action is inherently uncertain. I can never pin down how my actions reduced global emissions because there's a high chance of failure. But if we pool the probability of the efforts of thousands of people, we can collectively say that we very likely made a big dent in emissions because we can spread the risk over many donors, activists, and professionals.

My intuition is the situation is different for larger organizations and donors who can afford to spread their bets and can do more to single-handedly galvanize an area of research or policy.

How would you feel about donating to a high-impact grantmaking funds that spreads their climate bets and works to advance entire areas or research and policies? If you donate to the Founders Pledge climate fund or the Giving Green fund, for example, your donations are pooled with those of hundreds to thousands of other people who collectively can spread their bets and build new areas or research and policy.

(PS. Setting up donations to funds instead of individual charities has some additional benefits. It helps to allocate funding to charities when they need it most, they can make grants to individual projects that don't need continuous funding, and funds can change their recipients if other charities become more effective. See the EE website.)

Thank you Trym!

Good point on the error sensitivity. The geometric mean penalizes low scores more so it increases the probability of a false negative/type II error: an alternative that should be prioritised is not prioritised.

Glad to hear!

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