Probabilistic projections of baseline (with no additional mitigation policies) future carbon emissions are important for sound climate risk assessments. Deep uncertainty surrounds many drivers of projected emissions. Here we use a simple integrated assessment model, calibrated to century-scale data and expert assessments of baseline emissions, global economic growth, and population growth, to make probabilistic projections of carbon emissions through 2100. Under a variety of assumptions about fossil fuel resource levels and decarbonization rates, our projections largely agree with several emissions projections under current policy conditions. Our global sensitivity analysis identifies several key economic drivers of uncertainty in future emissions and shows important higher-level interactions between economic and technological parameters, while population uncertainties are less important. Our analysis also projects relatively low global economic growth rates over the remainder of the century. This illustrates the importance of additional research into economic growth dynamics for climate risk assessment, especially if pledged and future climate mitigation policies are weakened or have delayed implementations. These results showcase the power of using a simple, transparent, and calibrated model. However, the simplicity of the model structure imposes severe caveats that point to research needs.
翻译:未来碳排放量的概率预测(没有额外的缓解政策)对于良好的气候风险评估十分重要。 预测排放的很多驱动因素都存在深刻的不确定性。 在这里,我们使用一个简单的综合评估模型,根据世纪尺度的数据和专家评估基准排放量、全球经济增长和人口增长,对2100年的碳排放量进行概率预测。 根据关于化石燃料资源水平和去碳化率的各种假设,我们的预测大致同意当前政策条件下的若干排放预测。我们的全球敏感性分析确定了未来排放不确定性的若干关键经济驱动因素,并显示了经济和技术参数之间重要的更高层次的相互作用,而人口不确定性则不那么重要。我们的分析还预测了本世纪剩余时间全球经济增长率相对较低的情况。这说明了对经济增长动态进行更多研究对于气候风险评估的重要性,特别是在承诺和未来气候减缓政策被削弱或推迟执行的情况下。这些结果展示了使用简单、透明和校准模型的力量。然而,模型结构的简单性带来了严重的洞穴,表明研究需要。