The authors did a great job, they processed a huge amount of badly structured information from the national inventory reports in pdf format and other datasets. The data they compiled and estimated on uncertainties of the National GHG emission estimates submitted by countries to the UNFCCC and for the Global Multi-Regional Input-Output analysis are new, at least I did not find similar data. The data are useful for researchers as well as for policy makers because knowing the uncertainties is as important as knowing the values themselves.

The manuscript contains a very detailed description of the method, results, and a good discussion of limitations. However, the manuscript contains so many details (e.g. explanation of what is Annex-I parties) that sometimes it is challenging to track the most important information. I would suggest moving all the auxiliary information to the footnotes. Otherwise, everything is described very well.

I have two specific comments:

Could you please clarify what is WM in paragraph 485?

Could you please correct the legend in Figure E2 (p.38)?

Data quality.

Unfortunately, I failed with running the R script developed by the authors. I could not find the package mRio. Probably, it is my fault as I'm not an experienced user of R.

I looked at some of the data separately. However, it would be good if the authors developed a script for exploring the data that does not require special packages.

I could not estimate the quality of the data comprehensively. I put major revisions because of my problem with the R script and, as a result, missing comprehensive data quality assessment.

This is a good paper. Uncertainties in GHG accounts (production- and consumption-based) is hot topic and very high on the agenda of modellers and decision makers alike. The authors do a great job in shedding light on various issues. I was not aware that uncerainty estimates of the UNFCCC data set did exist in the NRI. This paper is clearly contributing to leveraging this infromation and providing it to a wider audience. The findings are relevant in highlighting the sectors and regions that are of concerne. Using Dirichlet distributions to capture correlations in data disaggregation is a smart move. Using entropy approaches in setting the paramters of the Dirichlet distribution is even smarter. I like that approach. The GHG extensions and uncertainties and meta data provided by the authors is very useful. I suggest this paper for publication after a minor revision.

1. So how did the EXIOBASE team construct the GHG extensions? I am sure the answer to that question is already contained in the manuscript but I would love to see this stated more clearly. Is the EXIOBASE extension based on UNFCCC alone? This is not clear to me after reading the text and when MC results are compared to the original EXIOBASE extension are was wondering what I am lookig at here.

2. You have all this information on uncertainties. Would it be possible for you to suggest a sector classification for GHG footprins from EXIOBASE that some incorporates that insights on uncertainties. I mean you implicitly say that assessing GHG footprints on the level of 163 sectors, is for many sector not a good idea due to the uncertainties. And yes, I am aware that such a decision depends greatly on the research question. However, if one aims at an analysis of the GHG footprints of sectors for a given country, what would be a robust sector aggregation that somehow "outsources" or "collects" the more problematic sectors in a lets call it "highly uncertain" sector group (something like products nec). This would be of high value for analysts. Maybe that is beyond the scope of your paper, but I think you are highly qualified to give such a suggestion or at least some reflections.

3. You provide a good overview on the different approaches to estimating uncertainties of the production-based emissions accounts (heuristics vs. modelling with power series regressions): Am I right in assuming that all power series regressions actually rely on the "law of large numbers"? Moreover, looking the MC results for sectors and countries, it seems to me you arrive at similar findings that could be understood as supporting the law of large numbers assumption: The larger a sector or region the smaller the uncertainty in relative terms. I don't want you to reframe the introduction but I was wondering whether to a ceratin degree the law of large numbers and more simplistic approach are actually justified.  Would be interesting to see some reflections how your findings connect to that.

4. I am confused by the terms "classification" and "categories" in the context of the UNFCCC data. See Figure 3 for example. Does category stand for process/industry and classification for the type GHG emissions i.e. flow? I am confused. Please clarify that somehow a bit better.

5. You make a great job in detailing the importance of how to allocate international road transport, especially for european countries. I would love to read a bit more about that in your reflections. Is there something you would recommend to modellers that are dealing with the same problem (best practice)? This would be a nice to have, not a must-have. I think this is really another important intervention point for dealing with uncertainties in the future, which deserves a bit more attention.

The DOI link you give to your GHG extensions (https://doi.org/10.5281/zenodo.10037713 ) is forwarding me to the UNFCCC uncertainty data set (https://zenodo.org/records/10037714 ). Is this meant to be like that? Please check the links.

All in all, a well written and interesting piece of work. I congratulate the authors.