Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
The social cost of carbon (SCC) is a crucial metric for informing climate policy, most notably for guiding climate regulations issued by the US government. Characterization of uncertainty and transparency of assumptions are critical for supporting such an influential metric. Challenges inherent to SCC estimation push the boundaries of typical analytical techniques and require augmented approaches to assess uncertainty, raising important considerations for discounting. This paper addresses the challenges of projecting very long-term economic growth, population, and greenhouse gas emissions, as well as calibration of discounting parameters for consistency with those projections. Our work improves on alternative approaches, such as nonprobabilistic scenarios and constant discounting, that have been used by the government but do not fully characterize the uncertainty distribution of fully probabilistic model input data or corresponding SCC estimate outputs. Incorporating the full range of economic uncertainty in the social cost of carbon underscores the importance of adopting a stochastic discounting approach to account for uncertainty in an integrated manner.