the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 02 Jun 2025
Validation of climate mitigation pathways
Abstract. Integrated assessment models (IAMs) are crucial for climate policymaking, offering climate mitigation scenarios and contributing to IPCC assessments. However, IAMs face criticism for lack of transparency and poor capture of recent technology diffusion and dynamics. We introduce the Potsdam Integrated Assessment Modeling validation tool, piamValidation, an open-source R package for validating IAM scenarios. The piamValidation tool enables systematic comparisons of variables from extensive IAM datasets against historical data and feasibility bounds, or across scenarios and models. This functionality is particularly valuable for harmonizing scenarios across multiple IAMs. Moreover, the tool facilitates the systematic comparison of near-term technology dynamics with external observational data, including historical trends, near-term developments, and stylized facts. We apply the tool to the integrated assessment model REMIND for near-term technology trend validation, demonstrating its potential to enhance transparency and reliability of IAMs.
Competing interests: One of the authors, Gunnar Luderer, serves as a Topic Editor for this journal. All other authors declare no competing interests.
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 preprint. The responsibility to include appropriate place names lies with the authors.- Preprint
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Status: open (until 28 Jul 2025)
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RC1: 'Comment on egusphere-2025-2284', Anonymous Referee #1, 04 Jun 2025
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This reviewer's understanding of piamValidation and the study presented in 'Validation of climate mitigation pathways' is that the package serves as a crucial tool for improving the reliability of Integrated Assessment Models (IAMs). While IAMs are essential for shaping climate policy, they often face criticism for their lack of transparency and their limited ability to account for real-world technological advancements. piamValidation aims to address these concerns by systematically comparing IAM scenario data against historical observations, feasibility limits, and other model results. The package is designed for ease of use, requiring minimal coding to generate interactive HTML reports with heat maps, which encourages broader adoption and the development of more realistic near-term scenarios. Its effectiveness is demonstrated using the REMIND model, highlighting its ability to detect emerging technological trends that diverge from expected patterns—such as developments in carbon dioxide transport and storage, electric vehicles, and offshore wind power. Clear visual feedback, including 'traffic light' evaluations, helps model developers implement meaningful improvements.
If this interpretation is correct, then this reviewer has identified weaknesses and several areas for improvement.
First, the scope of validation variables and case studies appears limited. The current application primarily focuses on select technologies and the REMIND model. To enhance the tool's applicability and robustness, this reviewer suggests expanding its scope to include other sectors, variables and case studies with different IAMs (e.g. MESSAGE, GCAM; https://www.ngfs.net/ngfs-scenarios-portal/glossary/#IAM). A broader range of applications would help demonstrate the versatility of piamValidation and strengthen its reliability across diverse modeling frameworks.
Second, the effectiveness of the validation is inherently dependent on data quality and methodological transparency. The reliability of the process hinges on the accuracy and robustness of observational and benchmark data. This reviewer calls for a more detailed discussion on managing uncertainties in reference datasets, along with a deeper technical explanation of how validation thresholds are determined, particularly for complex or uncertain data. In addition, this reviewer suggests that the authors incorporate metadata quality indicators for input reference datasets and establish a shared, moderated repository of standard validation thresholds to enhance transparency and reproducibility.
Third, technical barriers and user accessibility warrant further consideration. While piamValidation is designed for ease of use, its reliance on R and the IAMC data format may present challenge*s for users unfamiliar with these tools. To broaden accessibility, this reviewer suggests providing more guidance for non-R users or exploring interfaces for alternative platforms, such as Python. Expanding compatibility across multiple programming environments would help ensure that a wider audience - including researchers and policymakers with varying technical backgrounds - can effectively use the tool.
Fourth, the future directions are not entirely clear. While ongoing development is mentioned, a more structured roadmap outlining planned enhancements would be beneficial. This reviewer suggests specifying future improvements, such as incorporating machine learning techniques, expanding the range of validation variables and integrating the tool with additional modelling frameworks. In addition, extending validation metrics to assess long-term feasibility and policy robustness would strengthen the tool’s relevance for decision-making in climate policy.
Fifth, the discussion on tool limitations lacks depth. A more comprehensive examination of potential biases, uncertainties and challenges in applying piamValidation across diverse IAMs would strengthen the manuscript. This reviewer suggests providing clearer guidelines for identifying and mitigating these issues, ensuring that users can navigate the tool’s constraints effectively. Addressing these limitations in greater detail would enhance transparency and reinforce the reliability of validation outcomes.
Finally, additional specific comments are provided in the annotated manuscript file.
This reviewer offers an overall endorsement and recommends acceptance, contingent on minor revisions to address the areas for improvement outlined above. These revisions would further enhance the paper’s contribution to strengthening the credibility of IAMs.
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