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.

碳的社会成本 (SCC) 是告知气候政策的关键指标，尤其是用于指导美国政府颁布的气候法规。假设的不确定性和透明度的表征对于支持这种有影响力的指标至关重要。SCC 估计固有的挑战突破了典型分析技术的界限，需要增强的方法来评估不确定性，从而提高贴现的重要考虑因素。本文讨论了预测非常长期的经济增长、人口和温室气体排放以及校准贴现参数以与这些预测保持一致的挑战。我们的工作改进了替代方法，例如非概率场景和不断贴现，政府已使用但未完全表征完全概率模型输入数据或相应 SCC 估计输出的不确定性分布。将所有经济不确定性纳入碳的社会成本，强调了采用随机贴现方法以综合方式解释不确定性的重要性。