GCAM-Europe v7.2.0: Enhancing Policy-Relevant Climate Modelling Through Spatial and Sectoral Detail

Sampedro, Jon; Horowitz, Russell; Rodés-Bachs, Clàudia; Van de Ven, Dirk-Jan

doi:https://doi.org/10.5194/egusphere-2025-3546

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https://doi.org/10.5194/egusphere-2025-3546

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11 Aug 2025

| 11 Aug 2025

Status: this preprint is open for discussion and under review for Geoscientific Model Development (GMD).

GCAM-Europe v7.2.0: Enhancing Policy-Relevant Climate Modelling Through Spatial and Sectoral Detail

Jon Sampedro, Russell Horowitz, Clàudia Rodés-Bachs, and Dirk-Jan Van de Ven

Abstract. Integrated Assessment Models (IAMs) serve as critical instruments for scenario-based analysis and have been instrumental in informing environmental policy at both global and regional scales. However, their limited geographical and sectoral scope constrains their ability to evaluate comprehensive policy packages such as the European Union's Fit-for-55. GCAM-Europe, an expansion of the well-stablished Global Change Analysis Model (GCAM) addresses this gap by explicitly representing energy, land use, agriculture, water, and emissions systems for European Member States and key non-EU countries. Operating within a global framework, the model enables integrated assessment of policy impacts both across and within European regions, while also capturing spillover effects in other regions in the world. As an open-access and continuously evolving platform, it provides a valuable tool for European researchers, policymakers, and stakeholders to design, test, and evaluate climate and environmental strategies that support a just and effective climate transition.

Received: 23 Jul 2025 – Discussion started: 11 Aug 2025

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.

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Jon Sampedro, Russell Horowitz, Clàudia Rodés-Bachs, and Dirk-Jan Van de Ven

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RC1:
'Comment on egusphere-2025-3546', Anonymous Referee #1, 03 Sep 2025 reply
This article presents an enhanced regionalized version of GCAM, focused on Europe, and compares it to the standard GCAM regional representation. The reviewer acknowledges the substantial effort required to develop and document this contribution, both from a modelling and data perspective.
Given that regional detail is crucial for accurately representing national decarbonization strategies, the topic addressed is scientifically relevant, and the improvements presented can significantly increase the scientific and political value of the GCAM Integrated Assessment Model applications.
However, the comparison between the standard and extended geographic formulations is presented in a largely descriptive manner, with limited critical assessment. The manuscript does not sufficiently address the trade-offs between the two approaches.
While results are reported at both European-aggregated and individual country levels, the core focus of the paper is on comparing geographic formulations. A more systematic comparison would therefore come from contrasting standard GCAM regional results with the aggregation of the Europe-detailed version.
Several figures (e.g., Figures 4, 6, 7, 8, 9, 10, 11, 13, 14, and 16) would more strongly serve the paper’s purpose if they showed comparisons at the five core European regions used in the standard GCAM formulation, rather than only country-level outcomes from the detailed model. Country-level results could be moved to supplementary material or omitted unless they demonstrate dynamics relevant for the comparison. The main text should then focus on differences across regions, highlighting benefits, trade-offs, or distinct dynamics revealed by the European-detailed formulation.
The authors briefly note only in the conclusion section that computational demands increase, that the model is more sensitive, and that it is less robust to the addition of new features. These points deserve fuller discussion in the main text, ideally in a dedicated section rather than only in the conclusions. For instance: What does “more sensitive” mean in practice (e.g., instability, difficulty converging)? In what ways is robustness reduced? How were these issues identified, and how might they be addressed?
The paper would benefit from a deeper discussion of the practical implications of higher geographic detail. Specifically:
How does the dimensionality of the problem change?

Does the model require significantly more time or memory to solve?

Is there a trade-off between adding geographic detail and maintaining detail in other dimensions (e.g., technology, resources)?

Would this be a step towards replacing the standard GCAM formulation with a higher geographical detail version or the benefits for other regions are not sufficient to justify such change? Which are the steps or processes necessary to achieve such goal?

Additionally, it would be valuable to clarify which challenges are specific to the EU disaggregation exercise and which can be generalized to other GCAM or IAM regional disaggregation efforts. Lessons learned and recommendations for future work would further increase the impact of the study.
Additional comments:
The introduction could better situate this work in the broader IAM literature, including references to other models’ approaches to geographic detail and to applications in IPCC assessments.

Further clarification is needed regarding the multilevel Armington trade formulation:
How are elasticities of trade determined?

Are there differences between EU and member state parameterizations and methods?

How do the two Armington levels interact?

Was any parameter adaptation required for consistency across levels?

Are there published literature describing this formulation?

The authors should clarify whether the improved building-sector coverage could (or should) be integrated into the core GCAM formulation, and whether data availability is a limiting factor for that.

Regarding consumer heterogeneity in the residential energy sector, could the authors cite the data sources and assumptions underlying the use of income deciles? Since Household Budget Surveys (HBS) are only proposed for future work, mentioning it in the buildings results analysis serves little purpose for the paper discussion in this reviewer opinion and it would be more appropriate to be part only of the conclusions section as a suggestion for extensions rather than in the main results.

For socioeconomics, it would be helpful to briefly note challenges of extending this formulation beyond SSP2.

Some sentences lack context, such as: “This reduction is particularly pronounced in several Scandinavian countries such as Denmark, Sweden, and Finland, where CO2 emissions fall by more than 250% by 2100” Relative to which year or scenario? Absolute values or clearer framing would improve readability.

The authors mention moving the calibration year to 2021 as future work. What calibration year is used in the present paper?

Results discussion
Biomass: Since biomass use increases, the paper should clarify how much is domestically sourced versus imported when it is first metnioned. Europe’s limited biomass potential is documented in other literature (e.g., Ruiz et al. 2019, ENSPRESO database, https://doi.org/10.1016/j.esr.2019.100379), and results should be contextualized against such benchmarks.

Industry: Is industrial leakage to other regions considered? Is there any change between the formualtions?

Electricity: Why does wind’s share decrease so substantially by 2050? Which technologies replace it, and why? Is this due to costs, learning-by-doing, or artifacts of regional disaggregation?

Land use: What drives changes in grazed vs. non-grazed pastures, and in managed vs. unmanaged forests? Are these cost-driven or flat solution space artefacts of the land-use representation?

Emissions: presenting absolute values of country emissions in the new formulation provides limited insight. Relative emission shares and changes across regions between the two model formulations would be more informative for assessing how burden-sharing dynamics shift under increased geographical detail.

Trade effects: Since the authors state that the multi-level Armington formulation influences import patterns for goods like ammonia, iron, steel, and so on; it would be important to also present in the paper marginal costs (or prices) and the imports levels versus internal production, so we could confirm if the change in traded amounts is substantial or not to allow the comparison between both scenarios results. Is trade is responsible to too much change in the energy sector, the comparison between both scenarios provide less insight, and the changes are mainly driven by the different Armington trade formulation, instead of the increased internal detail of the energy system.

Results realism: The policy scenario results attribute increased nuclear expansion to Germany and France. Is this politically realistic given Germany’s nuclear phase-out? Were political or legislative constraints considered for nuclear and other technologies?

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Citation: https://doi.org/10.5194/egusphere-2025-3546-RC1

Jon Sampedro, Russell Horowitz, Clàudia Rodés-Bachs, and Dirk-Jan Van de Ven

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https://doi.org/10.5194/egusphere-2025-3546-supplement

Jon Sampedro, Russell Horowitz, Clàudia Rodés-Bachs, and Dirk-Jan Van de Ven

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Short summary

GCAM-Europe is an open-access modelling tool designed to assess the impacts of climate and environmental policies across European countries. Built on a global framework, it adds detailed regional and sectoral representation for Europe, including energy, land use, water, and emissions. This allows for analysis of policy effects within and between countries, as well as global spillover effects, to support transparent, evidence-based decision-making.


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