the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
DSCALE v0.1 – an open-source algorithm for downscaling regional and global mitigation pathways to the country level
Abstract. Integrated Assessment Models (IAMs) provide low-carbon scenarios at a global scale or for broad economic aggregates, as running these models for every country would be computationally demanding. Lack of national results from IAMs, hinders the enhancement of NDCs (Nationally Determined Contributions) and LTS (Long Term Strategies) in accordance with the 1.5C target and best available science. To address this limitation, we have developed DSCALE (Downscaling Scenarios to the Country level for Assessment of Low carbon Emissions), a novel algorithm designed to downscale regional IAMs outcomes to the country level. In this paper we present the methodology and show results for both current policy and 1.5°C scenarios from the NGFS 2023 release. This downscaling tool provides insights for energy and emission developments and targets at the country level consistent with global scenarios from IAMs. Moreover, this tool facilitates the integration of IAMs results with other models and tools requiring energy and emissions data at the country level, such as the macroeconomic NiGEM model.
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Status: open (until 16 Jul 2025)
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RC1: 'Comment on egusphere-2025-121', Anonymous Referee #1, 15 Jun 2025
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The paper is very useful and timely research.
This would provide a very useful methodology for downscaling climate mitigation scenarios.Here are some comments and suggestions:
"Estimated emissions gap of 19-27 MtCO2 in 2030" (Line 27) looks too small and needs to be checked.
Line 149~152 - IAM-Driven Path is assumed to use country-specific GDP and population pathways from the SSPs. Does this mean that National Data-Driven Path does not use country-specific GDP and population pathways from the SSPs? Then what projections are used for country-specific GDP and population pathways under National Data-Driven Path?
Some descriptions and formulas are difficult to understand. Some examples are as follows:
From Line 219,
For total final energy, the “MAIN” sector coincides with “GDP”.
FEN_t,c,s,e = EI_t,c,s,e MAIN_t,c,s,e --- (5)What does MAIN_t,c,s,e of (5) mean? Is it GDP? Then, what does GDP for t, c, s, e mean? How is the GDP for sector s and energy e defined?
Form Line 259, the authors say that we need to assign a “MAIN” sector for each final energy variables, using a hierarchic structure. When downscaling total final energy, we use the GDP as the main sector. Then, we use total final energy as the main sector for each of the energy carriers, essentially calculating a percentage share.. This explanation is difficult to understand.With the Alphas and Betas for functional forms (equation 6) estimated based on historical data or IAM scenarios results, how can the time of conditional convergence (tc) as suggested in the paper, such as 2100, 2150, 2200, applied?
The explanation for Fugure 3 says that the graph displays a range of downscaled results for New Zealand (blue lines), Japan (light blue), and Australia (green lines) under different convergence criteria (fast, medium, and slow) but it is not possible to idenify lines for eash of different convergence criteria (fast, medium, and slow).
PLATTS database (2019) is not available. Need more information on this database.
The default weights for the criteria for downscaling (Table 5) needs more justification and the mechaniem for weight design needs to be developed.
Citation: https://doi.org/10.5194/egusphere-2025-121-RC1
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