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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.08049 |
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| _version_ | 1866917395216465920 |
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| author | Zhang, Fangyuan |
| author_facet | Zhang, Fangyuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_08049 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Quantifying Decarbonization Speed Across Climate Scenarios Zhang, Fangyuan Applications 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. |
| title | Quantifying Decarbonization Speed Across Climate Scenarios |
| topic | Applications |
| url | https://arxiv.org/abs/2604.08049 |