Assessments of decreases in economic damages from climate change mitigation typically rely on climate output from computationally expensive pre-computed runs of general circulation models under a handful of scenarios with discretely varying targets, such as the four representative concentration pathways for CO₂ and other anthropogenically emitted gases. Although such analyses are valuable in informing scientists and policymakers about massive multilateral mitigation goals, we add to the literature by considering potential outcomes from more modest policy changes that may not be represented by any well-known concentration pathway. Specifically, we construct computationally efficient Quasi-representative Concentration Pathways (QCPs) to leverage concentration pathways of existing peer-reviewed scenarios. Computational efficiency allows for bootstrapping to assess uncertainty. We illustrate our methodology by considering the impact on the relative risk of mortality from heat stress in London from the United Kingdom’s net zero emissions goal. More than half of our interval estimate for the business-as-usual scenario covers an annual risk at least that of a COVID-19-like mortality event by 2100. Success of the UK’s policy alone would do little to mitigate the risk.

对缓解气候变化造成的经济损失减少的评估通常依赖于在具有离散变化目标的少数情景下（例如 CO ₂的四个代表性浓度路径）的大气环流模型的计算成本高昂的预计算运行的气候输出和其他人为排放的气体。尽管此类分析对于让科学家和决策者了解大规模的多边减缓目标很有价值，但我们通过考虑更温和的政策变化的潜在结果来增加文献，这些变化可能无法由任何众所周知的集中路径代表。具体来说，我们构建了计算效率高的准代表性浓度路径 (QCP)，以利用现有同行评审场景的浓度路径。计算效率允许引导以评估不确定性。我们通过考虑英国净零排放目标对伦敦热应激导致的死亡相对风险的影响来说明我们的方法。