🤖 AI Summary
This study addresses the current lack of a unified and transparent metric for quantifying and comparing decarbonization rates across diverse integrated assessment model (IAM) climate scenarios. The authors propose a concise, interpretable numerical indicator that, for the first time, enables systematic comparison and ranking of implied decarbonization speeds across 126 IAM scenarios. By integrating statistical analysis, empirical distribution construction, parametric fitting, and bootstrap resampling, the methodology rigorously quantifies uncertainty and validates consistency with representative concentration pathway assumptions. The resulting estimates—providing means, medians, and confidence intervals for decarbonization rates under each scenario—offer a robust and scalable foundation for evaluating climate policy pathways.
📝 Abstract
In this work, we analyze 126 publicly available IAM climate scenarios modeled by six leading teams in climate science. We define a simple numerical metric that measures the decarbonization speed implied by each IAM scenario. With this metric, the narrative based, high-dimensional time series scenario datasets can be ranked and compared in a transparent way. We find that the ranking of IAM scenarios according to the decarbonization speed is consistent with their representative concentration pathway assumptions, showing that the decarbonization metric is a useful summary of a scenario's mitigation policy. We further construct an empirical distribution and a fitted parametric distribution of the decarbonization speed estimates. Key statistics such as mean, median and their confidence intervals by the bootstrap resample technique are also reported.