Quantifying Decarbonization Speed Across Climate Scenarios

TL;DR

This paper introduces a simple metric to quantify and compare the decarbonization speed across 126 climate scenarios, aiding transparent scenario ranking and policy assessment.

Contribution

It defines a novel numerical metric for decarbonization speed, enabling transparent ranking and comparison of climate scenarios based on mitigation policies.

Findings

Decarbonization speed ranking aligns with RCP assumptions.

Constructed empirical and parametric distributions of decarbonization speeds.

Reported key statistics with bootstrap confidence intervals.

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.