Srikrishnan Vivek, Guan Yawen, Tol Richard S J, Keller Klaus
Department of Biological & Environmental Engineering, Cornell University, Ithaca, NY USA.
Department of Statistics, University of Nebraska, Lincoln, NE USA.
Clim Change. 2022;170(3-4):37. doi: 10.1007/s10584-021-03279-7. Epub 2022 Feb 24.
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. While the simple model structure has several advantages, it also creates caveats for our results which are related to important areas for further research.
The online version contains supplementary material available at 10.1007/s10584-021-03279-7.
基线(无额外缓解政策)未来碳排放的概率预测对于合理的气候风险评估至关重要。预计排放的许多驱动因素存在深度不确定性。在此,我们使用一个简单的综合评估模型,该模型根据世纪尺度数据以及对基线排放、全球经济增长和人口增长的专家评估进行校准,以对到2100年的碳排放进行概率预测。在关于化石燃料资源水平和脱碳率的各种假设下,我们的预测在很大程度上与当前政策条件下的几种排放预测一致。我们的全球敏感性分析确定了未来排放不确定性的几个关键经济驱动因素,并显示了经济和技术参数之间重要的高层相互作用,而人口不确定性则不太重要。我们的分析还预测了本世纪剩余时间内相对较低的全球经济增长率。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,特别是如果承诺的和未来的气候缓解政策被削弱或实施延迟。这些结果展示了使用简单、透明且经过校准的模型的力量。虽然简单的模型结构有几个优点,但它也给我们的结果带来了一些需要进一步研究的重要领域相关的注意事项。
在线版本包含可在10.1007/s10584-021-03279-7获取的补充材料。