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.

In order to limit temperature rise, it is essential to explore pathways for reducing greenhouse gas emissions. The methodology presented in this paper is considered valuable in contributing to the transparency of scenarios derived from integrated assessment models (IAMs) that estimate future GHG emission pathways. Therefore, the paper is deemed worthy of publication.

However, the following points should be addressed prior to publication.

• The development of integrated assessment models (IAMs) to support greenhouse gas mitigation strategies in developing countries is expected to become increasingly important.
  
  International databases such as those provided by the IEA are widely referenced in the development of IAM scenarios in developed countries. However, a key challenge going forward is how researchers in developing countries can secure access to such existing data, and how they can collect and make use of country-specific information on technologies and socio-economic conditions.
  
  Therefore, if the piamValidation proves effective in enabling researchers in developing countries to conduct practical scenario development, it would be of considerable significance. In this regard, it would be highly valuable for readers if the paper could elaborate on how such existing data can be utilized by researchers in developing countries.

• The paper lacks sufficient explanation as to why CCS (Carbon Capture and Storage) is considered a viable option in short- and medium-term scenarios.
  
  In both the United States and Australia, several CCS projects have failed. According to a report by the U.S. Government Accountability Office (GAO), seven out of eight CCS projects supported by the Department of Energy (DOE) were canceled. The only project that became operational, Petra Nova, saw NRG Energy withdraw from the project, with JX Nippon acquiring full ownership and shifting to a sole operation structure. Although the facility was restarted in September 2023, its commercial viability remains uncertain. CCS alone does not generate profit. Moreover, if the oil produced through Enhanced Oil Recovery (EOR) is combusted, additional CO₂ is emitted, raising questions—particularly from a life cycle assessment (LCA) perspective—about whether CCS via EOR leads to a net reduction in greenhouse gas emissions. The IEA’s Net Zero by 2050 scenario calls for the storage of 1.5 billion tonnes of CO₂ per year by 2030. However, the IEA itself acknowledges that achieving this would require “unprecedented investment and policy support” and that current progress is “seriously off track.” This paper also references the long lead time associated with CCS deployment.

           Given these points, analyzing CCS as a short- to medium-term mitigation option seems questionable. If CCS is to be proposed as a viable strategy within this timeframe, the paper should include a detailed explanation of where and under what conditions it could realistically be implemented.

• While the adoption of electric vehicles (EVs) is progressing in certain regions, there are still areas where uptake remains limited due to persistent consumer concerns such as high costs, range anxiety, and constraints in battery supply. How does the analysis in the paper account for these regional differences? Furthermore, for regions where challenges remain, can the authors provide proposals or suggestions for addressing these issues?

• Offshore wind power has also faced negative public perceptions, including concerns about low-frequency noise. More recently, rising material and labor costs have led to higher bid prices and an increasing number of project cancellations. How does piamValidation address or account for these challenges?

• To effectively reduce GHG emissions, demand-side mitigation plays a significant role. Although the paper focuses primarily on technologies, when comparing scenarios, it is also important to consider how demand-side reductions are treated. Please also address demand-side mitigation.

• The paper states that “IAM scenarios contribute to the IPCC assessments,” but the primary purpose of IAMs is to support policy formation, not to contribute directly to the IPCC.
  
  Indeed, IAM analyses are an important component of IPCC reports, and being reviewed and synthesized by the IPCC helps convey findings widely to policymakers, which is highly meaningful.
  
  However, it is important to clarify that the original role of IAM scenario development is to present options and impact assessments for real-world policy challenges, maintaining its independent purpose.

• "The explanation of how scenarios can be improved through the use of the piamValidation is insufficient."

• The statement “Early IAM applications date back to the late 1990s (Cointe et al., 2019)” appears to be inaccurate; it would be more appropriate to say “the late 1980s” or “the early 1990s.”
  
  For instance, James Edmonds and his colleague published Global Energy: Assessing the Future (Oxford University Press, New York) in 1985, in which they analyzed future pathways using a model.

             In addition, the IPCC First Assessment Report (AR1) published in 1990, the Supplementary Report published in 1992, and Climate Change 1994: Radiative Forcing of Climate Change and An Evaluation of the IPCC 92 Emissions Scenarios all included analyses of future GHG emissions pathways and their implications for temperature projections.

Furthermore, regarding the IMAGE model referenced by Cointe et al. (2019), IMAGE 1.0 was developed by Rotmans in 1990, and IMAGE 2.0 was edited by Alcamo in 1994.

The manuscript presents the development and application of piamValidation, an open-source R package aimed at enhancing the transparency and credibility of integrated assessment models (IAMs). This tool enables structured comparisons of scenario data against historical trends, feasibility bounds, and across models, thereby addressing well-known criticisms related to transparency and technological realism. The application to the REMIND model demonstrates its practical relevance and potential to strengthen confidence in IAM-based analyses. Given the importance of IAMs in shaping climate policy, systematic validation tools are highly valuable.

The manuscript would benefit from addressing the following points to strengthen its suitability for publication:

1)The current implementation of piamValidation is applied primarily to a limited set of technologies within the REMIND model. This narrow focus restricts the demonstration of the tool’s broader applicability. To enhance generalizability and robustness, the validation scope could be expanded to include a wider array of sectors, variables, and IAMs;

2)The discussion of the tool’s limitations is somewhat superficial. A deeper analysis of possible uncertainties, and the difficulties of applying piamValidation to various IAMs would enhance the strength of the manuscript.