the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 22 Apr 2026
Global and National CO2 Emission from Lime Production Process and Carbonation sink from 1930 to 2024
Abstract. Accurate quantification of both lime process emissions and carbonation sink is essential for the Global Carbon Budget (GCB). By extending temporal coverage (1930–2024), refining spatial resolution (11 major lime-producing countries), and expanding system boundaries (adding Blast Furnace Slag, BFS), this study constructs the first standardized dataset of lime CO2 process emissions and carbonation sink covering 81.09 % of global lime production. We estimate cumulative global lime process emissions are 15.29 Gt CO2 (95 % CI: 13.81–16.79 Gt CO2), with the construction and metallurgical sectors serving as primary sources, contributing 5.80 Gt CO₂ (37.92 %) and 5.04 Gt CO₂ (32.95 %), respectively. During the same period, cumulative lime carbonation sink reached 7.33 Gt CO₂ (95 % CI: 5.95–8.88 Gt CO₂), achieving a carbon offset ratio (cement carbonation sink to process emission) of 47.65 %, which is 8.32 % increase compared with Bing et al. (2023). The lime carbonation sink in 2024 accounted for approximately 1.5 %–2 % of the global terrestrial carbon sink in 2023. China is the main contributor, with cumulative emissions of 8.89 Gt CO₂ (58.13 % of the global total) and cumulative carbonation sink of 4.21 Gt CO₂ (57.45 % globally) from 1930 to 2024. Lime‑stabilized soil (LSS, 36.53 %), mortar (MOR, 18.66 %), steel slag (SS, 17.73 %), and blast furnace slag (BFS, 12.83 %) were the primary carbon uptake materials, collectively accounting for 85.75 % of the total carbonation sink. Significant regional disparities were pronounced: developed countries (e.g., those in Europe, the United States, Japan, and Australia) have already peaked in lime process carbon emissions, with net emissions gradually approaching zero. In contrast, developing countries such as China and Brazil continue to exhibit growth in both emissions and carbonation sink. Although their carbon‑offset levels exceed 50 %, they face substantial pressure to reduce total emissions. This dataset provides critical data for incorporating the lime carbonation sink into the Global Carbon Budget. It also contributes to optimizing global carbon modelling and regional carbon‑neutrality pathways. The dataset is archived on Zenodo https://doi.org/10.5281/zenodo.18616060 (Bing et al., 2026).
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Status: open (until 06 Jul 2026)
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CC1: 'Comment on essd-2026-218', Peiying Li, 22 May 2026
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CC2: 'Reply on CC1', Fengming Xi, 24 May 2026
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Thanks for your comments.
- (L25-27) The statement “which is 8.32% increase compared with Bing et al. (2023)” could be clarified further. It is recommended to explicitly specify the compared time period and baseline value, rather than only citing the previous study, to improve interpretability for readers.
Author Response: We sincerely thank for this constructive suggestion. We completely agree that explicitly specifying the baseline value and the compared time period significantly improves the interpretability of our findings for the readers. In response, we have revised the corresponding sentence in the Abstract to clearly include the 1930–2020 timeframe and the 38.83% baseline established by Bing et al. (2023). Additionally, we corrected a minor typo in the parentheses, changing "cement carbonation sink" to "lime carbonation sink" to ensure precise terminology. The corresponding modifications have been marked in red in the revised manuscript. The revised text in the Abstract now reads: "During the same period, cumulative lime carbonation sink reached 7.33 Gt CO₂ (95% CI: 5.95–8.88 Gt CO₂), achieving a carbon offset ratio (lime carbonation sink to process emission) of 47.65%, an 8.82 percentage point increase over the 38.83% baseline for 1930–2020 reported by Bing et al. (2023)."
- (L618-625) The citation format “based on (Xi et al., 2016) and (Bing et al., 2023)” is not recommended. It should be revised to a standard academic form, e.g., “Xi et al. (2016)” and “Bing et al. (2023)” or a unified parenthetical citation.
Author Response: We appreciate you pointing out this formatting oversight. We have revised the in-text citation format to align with standard academic conventions. Specifically, the text in this section has been corrected to "...based on the accounting model established by Xi et al. (2016) and the research framework of Bing et al. (2023)" The corresponding modifications have been marked in red in the revised manuscript.
- (L372-387) It is recommended to improve the referencing of supplementary datasets by clearly linking the updated data to the corresponding tables in this section.
Author Response: We appreciate this constructive recommendation. We fully agree that explicitly linking the updated data to the corresponding supplementary tables significantly improves the transparency and readability of the manuscript. In response, we have revised this section to include precise cross-references to the corresponding datasets (e.g., SI-2 Data 1, SI-3 Data 1–15, SI-2 Data 3, and SI-3 Data 9) immediately following the description of each of the four key refinements. Additionally, we have corrected a few minor grammatical typos in this paragraph to improve the overall flow. The corresponding modifications have been marked in red in the revised manuscript.
- (L323-329) The manuscript includes lime mortar carbonation, which is also commonly considered in cement carbonation sink studies. The authors are advised to further clarify the system boundary definition between lime mortar and cement mortar to avoid potential double counting across different mineral carbonation datasets, thereby ensuring more consistent integration into global carbon budget.
Author Response: We highly appreciate your rigorous attention to system boundary and the integration into the Global Carbon Budget. We confirm that there is absolutely no double counting, and we have updated the text to make this distinction explicitly clear. To ensure precise accounting, we treat lime mortar and cement mortar as completely separate material flows based on the origin of their active CaO. Existing cement carbonation sink datasets strictly account for the carbonation of hydration products derived from cement clinker. In contrast, the lime mortar (MOR) quantified in our study originates exclusively from commercial lime production. We have revised the methodology section to explicitly define this system boundary, clarifying that our lime mortar calculation relies solely on the proportion of commercial lime allocated to the construction sector, strictly excluding any CaO derived from cement clinker. The corresponding modifications have been marked in red in the revised manuscript.
- A major highlight of this paper is the inclusion of blast furnace slag (BFS) into the lime carbonation sink accounting for the metallurgical industry for the first time. However, on a global scale, a significant amount of blast furnace slag is utilized in cement production. It is unclear whether the carbon uptake accounting for BFS and SS in this study overlaps with existing cement accounting. If there is no double-counting, please explicitly clarify this in the manuscript to avoid readers’ confusion.
Author Response: We highly appreciate your rigorous attention to system boundaries. We confirm that there is absolutely no double-counting between our accounting of metallurgical slags and existing cement carbonation datasets, and we have explicitly addressed this from both a macro-boundary and a micro-parametric perspective in the revised manuscript. Macro System Boundary (L265-268): While Blast Furnace Slag (BFS) and Steel Slag (SS) are widely utilized as Supplementary Cementitious Materials (SCMs) in cement production, current global cement carbonation sink accounting strictly sets its boundary around the calcination and carbonation of cement clinker. To ensure maximum clarity, we have added an explicit boundary statement in Section 2.3 (L265-268), clarifying that our system boundary for metallurgical slags is strictly restricted to the carbonation of CaO originating exclusively from lime fluxes. Micro Parametric Isolation (L333–L343): Mechanistically, as described in our BFS calculation formula, we introduce a specific localization parameter, lbfs, which explicitly represents the proportion of CaO in the slag derived strictly from lime flux rather than other raw materials. By utilizing lbfs to mathematically filter and isolate the lime-derived CaO fraction, our model eliminates the risk of boundary overlap with clinker-based cement carbonation accounting. The corresponding modifications have been marked in red in the revised manuscript.
- The lime production in Italy and Germany between 1930 and 1958 was estimated using a multiple linear regression model. It is recommended to briefly specify in the main text which key independent variables were used in this regression model. This will help readers quickly understand the rationality behind the early historical data reconstruction.
Author Response: We sincerely thank this valuable suggestion. We fully agree that providing the specific independent variables directly in the main text significantly enhances the transparency and interpretability of our historical data reconstruction.
In response, we have updated Section 2.1.1 (L180) to explicitly state the predictive variables used in the multiple linear regression models. Specifically, we have added that the historical lime production model for Germany was fitted using national crude steel and cement production, while the model for Italy utilized crude steel and alumina production as key independent variables. The corresponding modifications have been marked in red in the revised manuscript.
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CC2: 'Reply on CC1', Fengming Xi, 24 May 2026
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CC3: 'Comment on essd-2026-218', Xiaoqian Song, 25 May 2026
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This study constructs a long-term (1930–2024) and high-resolution dataset of global lime production CO₂ emissions and carbonation sinks, covering 11 major lime-producing countries and including blast furnace slag (BFS) for the first time. The research topic is important, the method is rigorous, the data quality is high, and the results are reliable. It effectively fills the gap of lime carbonation sink in the global carbon budget and has important scientific significance and application value.
However, minor revisions are needed regarding data description, parameter explanation, figure annotation, and partial expression. After minor revisions, the manuscript meets the publication standards of Earth System Science Data.
- The manuscript involves many abbreviations and symbols in the equations; it is better to provide a list of abbreviations to let the readers easy to look up.
- Line 262-264: “Beyond the production stage, nine industrial byproducts and materials are incorporated across three sectors: metallurgy (SS, BFS, RM), chemicals (PCC, CS, SUG, LM), and construction (LSS, MOR)”. Why focusing on these nine industrial byproducts and materials? Do you have a reference to support the products selection?
- The format of equations should to be consistent. For example, Line 255 is (3), while others are labeled as “Eq (4)”. Also, there are many equations appears in the text, and not numbers. For example, an equation in Line 300 and all equations in section 2.3.2. Please double check the formattingrequirement of ESSD and revise them accordingly.
- There are several typo errors, such as “CO2”should be “CO2”.
- Section 3.4: Strengthen the mechanism explanation of the time lag effect in the discussion to clarify why historical carbon sequestration keeps rising.
- It is recommended to add “future work” to illustrate how to make the research better. For example, future scenario projections (2025–2050) will to studied to support global and national carbon neutrality goals.
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RC1: 'Comment on essd-2026-218', Robbie Andrew, 25 May 2026
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Review of "Global and National CO2 Emission from Lime Production Process and Carbonation sink from 1930 to 2024"
This article builds on Bing et al., 2023, adding further regional disaggregation and a longer period of analysis. It's good to see this work, but there are a lot of problems that need to be dealt with.
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Main points
------------ The method is highly unclear and many assumptions are stated without any effort to justify them.
- The methods used by the same authors to estimate historical lime production data are highly suspect, but the result is labelled as "verified data".
- Some data sources have been misused.
- The apparent design of the uncertainty analysis results in an artificially low uncertainty of the overall carbonation rates.
- Since a model is the starting point of the work, it would make more sense to describe the model and then describe how the model's parameters were populated, the opposite of the current order. There is much confusion with variables being collected without any explanation of what they mean or what they are for.-----------------
Detailed comments
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L38: Developing countries "face substantial pressure to reduce total emissions": What pressure? Internal or external? Many will disagree that developing countries "face substantial pressure". Either replace this with a more neutral statement ("must reduce total emissions") or support it with a citation that there is actual pressure from elsewhere.L54-55: The statement needs to make it clear that it is limited to the lime industry. Currently "global CO2 emissions" implies strongly that the context has changed.
L70: Naming one individual like this as a source of singular authority isn't appropriate. Just say "As has been noted". The citation you provide at the end of the sentence is sufficient to indicate who has noted this.
L96: "decarbonation" should be "decarbonization"
L127-129: The authors talk several times about the "utilization rate" of slags, but without explaining why this is important. Why is only utilized slag assumed to result in carbonation? What happens to anything that is not "utilized"? According to the equations, zero carbonation, but why? Why should slag that is not "utilized" not undergo carbonation?
L133: The authors make it sound like the main goal is being included in the GCB dataset, rather than producing best-possible estimates of global lime carbonation. The points set out in this paragraph are not requirements (stated or otherwise) of being included in the GCB dataset. Rather, thay are about making the dataset more comprehensive. That ought to be the goal of the study. Please reword.
L140-141: "to synchronize with the Global Carbon Budget timeframe": What does this mean? Which timeframe? The GCB is updated every year, and the only fixed time is the start year of 1750, which doesn't match anything in this study.
L150-151: "Dynamic Parameterization of Technological Evolution": It's unclear what this means. What is described here suggests that technology changes happened in all places at the same time, which seems very unlikely when we are discussing developed and developing countries through the course of the 20th century.
L174-175: "the verified dataset": Do the authors here simply mean "peer-reviewed"? When was the dataset of Bing et al 2023 "verified"? This statement misleads the reader into believing that the start point is already a gold standard and no more discussion is necessary. I am highly sceptical of the lime production "data" used by this study and the previous one before 1990. Please explain what this enormous amount of lime was used for during decades when most uses of lime were at very low scale and indeed please also explain how it was produced. The only plausible candidate I can conceive of is construction, since most other uses were probably extremely small (steel, sugar, etc.). If it mostly went to construction, how many new buildings each year does that imply, and is that realistic? Further, have the authors considered how much energy would have been required to produce that much lime in those decades? Where did that energy come from, and how does it compare to estimates of China's total energy consumption at the time? These sorts of sense checks are critical when such methods have been used. Bing et al 2023 used ARIMA with no control variables to extrapolate the entire period 1930-1948. This sort of model has zero real-world constraints, and simply continues a trend as if the world were the same in the past, with the same construction needs, infrastructural capacity and energy availability, and assuming that the period 1949-1962 (already suspect) are robust. Adding wide uncertainty bounds to these estimates is insufficient. The estimates must be revisited, and the authors cannot just cite one of their own peer-reviewed articles as evidence of a "verified" dataset.
L177: Shimanish 2004 appears to only present data on sales of limestone to the lime sector, which the authors have transcribed directly (SI-2.xlsx, sheet Data1) as production of lime in Japan. This means that the numbers used by the authors are about double what they should be. I don't have time to check all sources, but this points to a fundamental misunderstanding of the input data being used. Are the data for Brazil, France, etc. also limestone, or are they really lime? I advise the authors to check their data sources again.
L177: IBGE, 2026: The reference is to a webpage that discusses briefly the role of IBGE in teaching, and has nothing about lime, lime data or data of any sort. A reviewer is left to guess how this ended up as the reference for this dataset.
L178: "Dominion Bureau of Statistics, 2026": This clearly refers to very old publications (Canada has not been a dominion for many decades), so the publication year is clearly not 2026. Replace 2026 with "various years" in this citation, since the data were presumably retrieved from many reports published over many years.
L179-180: It's unclear to me what the regression for Germany is supposed to be. The regression coefficients suggest a model of lime ~ steel + cement. This should be presented in the MS, not delegated to an SI file. The authors haven't explained why they use this model. In modern times, cement plants do not use lime, and any correlation between cement production and lime production is probably just a sign that both are needed in an economy, without indicating any direct link between the two. At certain points in the past German plants might have blended lime in their final product, but that would have changed dramatically over time. That a regression model produces a good fit to the training data does not mean that it is a good model for predicting earlier periods. The training period for Germany was from 1959, which is when new standards were introduced, curtailing the use of lime, but this training period has been used to predict earlier periods where very different behaviour exised. It is therefore very difficult to have any faith in these historical extrapolations.
L185-186: "specific allocation ratios": Are these temporally constant? If not, is that appropriate?
L216-208: How did the authors know the CaO contents for all these countries? Tier 2 means that data are available on the types of lime produced. Specifically, High-calcium lime (CaO + impurities), Dolomitic lime, and Hydraulic lime. I cannot see this in the MS nor in the SI data. Please clarify.
L229: I think it would make more sense here to present the method before the data. The authors are planning to use a model and then need to populate that with data. Please restructure the paper. In some fields, such as social science, one has a dataset that one wishes to explore, so one first discusses the dataset and then explains the methods one uses for that purpose. In this article, the authors start with a model and need data to feed into that model, which is very different. It then makes much more sense to start with the model description then explain the data used. That would help the reader's understanding.
L231: "gamma was set to 1, indicating complete carbonation during the production process": I'm not sure that "indicating" is the right word here. That gamma was set to 1 indicates only that all CaO is carbonated, not that it happens in the production process. Perhaps instead say why it is 1, i.e. that already during the production of these products, the lime is fully carbonated.
L232-235: No explanation of why R=1 was used for these categories. Since this is a critical parameter, and LSS is the largest share of lime carbonation in the study's results, this assumption must be justified. For example, without being an expert in these things, I struggle to see why LSS would have R=1 when much CaO is locked up more tightly in C-A-H and C-S-H, and even the CaO that is free as Ca(OH)2 has very little access to CO2 because it is highly compressed and generally under layers of asphalt or concrete. This is engineered soil (a term that the authors could add to aid understanding), and the goal is maximum stability, which means high compression. So how is R=1? Further, R "was assumed to be 1, representing complete carbonation within one year" seems incorrect. R=1 doesn't mean complete carbonation within one year; it means that maximum carbonation is reached within one year. It has to be combined with gamma to say how much that maximum is. The use of "complete" here is incorrect.
L234-235: "different kinetic models were applied": This is the only mention in the MS of the term "kinetic model", with no explanation. Why were different kinetic models applied? Some explanation to the reader would be helpful. Are the "kinetic models" those stylised assumptions depicted in Figure 1?
Fig 1: The diagram for the pile model suggests CO2 ingress only from the top and sides, yet also suggests just as much carbonation of the bottom layer. How is this consistent?
L249: Again, it's highly unclear what this timeline division actually means in practice. Which parameters were changed for each period? This is fundamental and should not be left to the reader trying to understand the method by studying the accompanying data files.
L255:256: The subscript clearly should not be "progress" but rather "process".
L274: It's now ten years since Xi et al 2016. Perhaps we should not expect readers to trawl back through literature to understand the process here. What does gamma actually represent physically? Is it actually meant to represent that the diffusion process in fact does not proceed according to Fick's law because the resulting CaCO3 reduces the ability of CO2 to enter the substance? I think many readers would appreciate having such extra insight.
L284: Not "including", since the authors present all of the options, not only some. Use a colon to introduce this complete list. And add "(depicted in Figure 1)" at end of sentence.
L316-318: This explanation of why the authors choose R=1 for LKD comes after they've already said twice that R=1. Please re-organise.
L327: Method 2 here says it uses Fick's law, but Method 3 doesn't mention it, even though it clearly uses it. Readers will be very confused by this point. Saying you use Fick's law in only one subsection implies you don't use it in the others.
L333-334: "U_ss denotes the ratio of stockpiling or roadbed utilization": Unclear. The ratio of stockpiling to roadbed utilization? The ratio of stockpiling utilization or the ratio of roadbed utilization? None of these interpretations are easy to understand. Do the authors mean the share of stockpiling in total? Or do they mean the share of stockpiling vs the share used in roadbeds? What differentiates these two uses? Is the mass of SS produced a function of the share that is stockpiled? Please reword for clarity.
L339: "following the same logic as SS", but the logic for SS was not explained either.
L359: Uncertainty assessment: I see no mention of whether any variables are assumed to be correlated. I take it that all variables are assumed to be uncorrelated, i.e. their errors are entirely independent of each other? In that case the authors use an uncertainty method (Montecarlo) that benefits the authors greatly in that the more they subdivide the total into separate variables, the smaller their final overall uncertainty assessment will be, by definition. That's how addition in quadrature works. If we start with a global estimate without subdividing by region, then we might say total lime production is 200 Mt (say) and relative uncertainty is 10% then our absolute uncertainty is ±20 Mt. If we then subdivide into four equal regions, each with 10% uncertainty, and we assume the errors are independent, then we add the errors in quadrature, so the absolute uncertainty would be sqrt(4x5^2)=10Mt and the relative uncertainty would be 10/200=5%. We have halved our uncertainty simply by subdividing the global into four regions, but only because we have assumed that the errors in these four regions are independent. The same problem arises when the use of lime is subdivided and errors are assumed uncorrelated, and so for any subdivision.
L363: Looking at sheet Data1, it is difficult to understand why in every case the minimum of the uncertainty range is higher than the maximum. This must surely be an error?
L369-371: "The 90% confidence intervals (CIs) were calculated using the percentile method; specifically, the 5th and 95th percentiles of the modeled results served as the lower and upper limits of the 90% interval." This is not a particular method; it is the definition of the 90% CI.
L375: "allowed to establish": replace with "allowed us to establish" or "allowed the establishment of".
L383-385: Now, after perhaps 3-4 times visiting the idea of "technological evolution", the authors provide a clue as to what that means. It is the utilization rates of steel slag and BFS that are assumed to change for each period? Presumably not both "production and utilization rates", since production rate is based on "data", not on assumptions of technological change? Why would utilization rates of slag change in the same way in developed countries as in developing countries during the 20th century? This set of assumptions is described without prefacing to say that data are not available so the authors make some assumptions to fill the data gap. Calling it "Dynamic Parameterization of Technological Evolution" it very flowery language that makes it sound much fancier than it really is. It's an assumption because data aren't available. I still struggle to see how the "industrial shifts" occurring in Europe and the USA in the 1930s would also have occurred in China of the 1930s, ditto "efficiency revolution" in the 1950s and 1960s.
L400-403: "Statistical analysis reveals a strong correlation between lime carbon emissions and lime production. However, the growth rate of process emissions was lower than that of lime production, suggesting that technological advancements have reduced the carbon emissions intensity of lime production": I struggle to understand this. The authors only have process emissions, so any change in emissions isn't about tech development, but just about types of inputs used. Since the numbers are entirely based on the data used, there should be no need to guess the cause of change ("suggesting"), and the authors should be able to explain definitively. The reason there is a strong correlation between lime carbon emissions and lime production is simply that the parameters in the model for deriving emissions from production vary to a very small degree, and one does not need after-the-fact statistical analysis to show this.
Fig 3: The uncertainty in China's process emissions here clearly indicates a problem. The method used before 1949 is based on the estimates from 1949 (ARIMA) but the value in 1948 has LOWER uncertainty here than the value in 1949. This cannot be. The uncertainties must be properly combined. The uncertainties in 1948 are a combination of the uncertainty from the ARIMA method and the uncertainty from the estimates they are trained on.
L688: "these regions face acute pressure": From whom? The implication here is that developed countries are exerting "acute pressure" on developing countries. Is that the authors' position?
L872: The author's name is rendered "Shimanishi" not "Shimnishi".
Citation: https://doi.org/10.5194/essd-2026-218-RC1 -
CC4: 'Comment on essd-2026-218', Lei Li, 28 May 2026
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This manuscript presents a potentially valuable dataset on global and national CO2 process emissions from lime production and associated carbonation sinks from 1930 to 2024. The topic is timely and relevant to Earth system carbon accounting, industrial decarbonization, and the possible inclusion of lime carbonation in global carbon budget assessments. The attempt to extend earlier work by increasing country-level resolution, incorporating blast furnace slag, and updating material-specific parameters is worthwhile.
However, the manuscript requires revision before it can be considered for publication.
1. Historical lime production data are reconstructed using a mixture of USGS data, statistical yearbooks, regression estimates, conversion ratios, and interpolation. The authors should provide much more detail on regression models, predictors, goodness-of-fit, uncertainty propagation, and validation.
2. Including BFS is an important contribution, according to the authors, but the system boundary requires clearer justification. The authors should explain how the lime-derived CaO fraction in BFS is determined.
3. The manuscript assumes that materials such as LSS, CS, and LKD can fully carbonate within one year. This assumption directly affects the estimated annual CO2uptake and may lead to overestimation if carbonation is actually slower.
4. The confidence interval terminology is inconsistent. The uncertainty section says that the authors used the 5th and 95th percentiles from the Monte Carlo simulations. This gives a 90% interval, not a 95% confidence interval. However, many results in the manuscript are reported as 95% CI.
Citation: https://doi.org/10.5194/essd-2026-218-CC4 -
CC6: 'Reply on CC4', Xiaoyu Zhang, 16 Jun 2026
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This manuscript presents a potentially valuable dataset on global and national CO2 process emissions from lime production and associated carbonation sinks from 1930 to 2024. The topic is timely and relevant to Earth system carbon accounting, industrial decarbonization, and the possible inclusion of lime carbonation in global carbon budget assessments. The attempt to extend earlier work by increasing country-level resolution, incorporating blast furnace slag, and updating material-specific parameters is worthwhile. However, the manuscript requires revision before it can be considered for publication.
We sincerely appreciate your constructive suggestions. The revised parts in the manuscript body have been highlighted in blue.
- Historical lime production data are reconstructed using a mixture of USGS data, statistical yearbooks, regression estimates, conversion ratios, and interpolation. The authors should provide much more detail on regression models, predictors, goodness-of-fit, uncertainty propagation, and validation.
Author Response: We thank the reviewer for this helpful comment. We have revised Sect. 2.1.1 to improve the transparency of the historical lime production reconstruction. Specifically, we clarified the hierarchical data-priority rule used in this study, including directly reported national statistics, national statistical yearbooks, industry association reports, USGS statistics, regression-based estimates, conversion-ratio estimates, and linear interpolation for short data gaps.
For Germany and Italy, we added details on the multiple linear regression models used to reconstruct lime production before 1959. Predictor variables were selected based on two criteria: their industrial linkage with lime consumption and the availability of continuous historical data. Candidate predictors included crude steel production, alumina production, paper and paperboard production, and construction-related activity indicators. For Germany, crude steel production was retained to represent metallurgical lime demand, while cement production was used only as a proxy for historical construction activity because long-term, consistent building-area statistics were not available for the full reconstruction period. We have clarified this proxy relationship in the revised manuscript to avoid implying a direct causal relationship between cement production and lime production. For Italy, crude steel and alumina production were retained because they represent major metallurgical and alumina-related lime-consuming sectors.
The regression outputs, including coefficient estimates, standard errors, p values, and R-square, are provided in Supplementary Table SI-2 Data 1. Given the limited availability of independent historical lime production records, model reliability was evaluated using these available regression diagnostics rather than independent external validation.
We also revised Sect. 2.4 to clarify how uncertainty from historical activity-data reconstruction was propagated in the Monte Carlo analysis. The revised text now describes the uncertainty treatment for directly reported statistics, regression-based estimates, conversion-ratio estimates, and interpolated values, including the use of appropriate probability distributions and the joint sampling of activity-data uncertainty terms with emission factors, CaO contents, material-use shares, carbonation ratios, and other parameters in the 10,000-iteration Monte Carlo simulation.
- Including BFS is an important contribution, according to the authors, but the system boundary requires clearer justification. The authors should explain how the lime-derived CaO fraction in BFS is determined.
Author Response: We thank for this important comment. We have revised Sects. 2.3 and 2.3.2 to clarify both the system boundary and the calculation of the lime-derived CaO fraction in BFS.
Based on the Material Flow Analysis framework of Liu et al. (2018a), the system boundary is defined according to the origin of CaO rather than the subsequent use of the slag. For SS and BFS, only CaO originating from lime material flow represented in the lime production activity data is attributed to the lime carbonation sink. CaO derived from iron ore, gangue minerals, limestone, dolomite, and other non-lime inputs is excluded.
Although some SS and BFS are subsequently used in cement or clinker production, the existing global cement carbonation datasets do not account for the carbonation of lime-derived CaO contained in these metallurgical slags, particularly BFS. Therefore, including this fraction in the present study does not result in double accounting.
We have also explained the determination of the lime-derived CaO fraction in BFS. An ironmaking burden mass-balance approach was applied. Quicklime consumption per tonne of pig iron was estimated using the total blast-furnace burden consumption, the proportions of sinter, pellets, and lump ore in the burden, and quicklime consumption per tonne of sinter. The resulting quicklime input was converted to lime-derived CaO and divided by the total CaO contained in the generated BFS, calculated from the BFS generation rate and its CaO content. The country- and period-specific parameters are provided in Supplementary Table SI-3 Data15.
- The manuscript assumes that materials such as LSS, CS, and LKD can fully carbonate within one year. This assumption directly affects the estimated annual CO2uptake and may lead to overestimation if carbonation is actually slower.
Author Response: We thank the reviewer for this important comment. We agree that the original wording conflated the temporal carbonation factor (R) with the material-specific maximum conversion factor (γ), and could therefore be interpreted as assuming complete conversion of all CaO within one year. We have revised the manuscript to distinguish these two parameters explicitly. R = 1 now denotes that the pathway-specific maximum carbonation extent defined by γ is reached within the first accounting year; it does not imply that γ = 1 or that all CaO is carbonated.
In Section 2.1.2, for CS and LKD, we retained the first-year accounting treatment but added its literature basis and limitations. Previous cement-carbonation studies allocate cement kiln dust uptake to the production year because its fine particle size and high surface-to-volume ratio promote rapid carbonation (Xi et al., 2016; Wu et al., 2024), consistent with experimental evidence for mineral carbonation of cement kiln dust (Huntzinger et al., 2009). Liu et al. (2018a) extended this approximation to powder-form LKD and summarized the high Ca(OH)₂ content and CO₂-fixation capacity of fine CS. We now state explicitly that the CS treatment remains a simplifying assumption and that slower field carbonation would shift part of the estimated uptake from the production year to subsequent years, particularly for recent production cohorts.
Most importantly, LSS has been removed from the first-year category. Because LSS is a compacted engineered soil in which carbonation progresses with CO₂ ingress, its time-dependent carbonation ratio is now calculated using a slab model with LSS-specific thickness and carbonation-rate parameters (Supplementary Table SI-3, Data 16). The corresponding LSS uptake calculation and related manuscript description have been revised accordingly. These changes avoid treating LSS as an instantaneously carbonating material and reduce the risk of overestimating its annual CO₂ uptake.
- The confidence interval terminology is inconsistent. The uncertainty section says that the authors used the 5th and 95th percentiles from the Monte Carlo simulations. This gives a 90% interval, not a 95% confidence interval. However, many results in the manuscript are reported as 95% CI.
Author Response: We thank for your pointing out this inconsistency. This was a typographical error in the uncertainty section. The confidence intervals reported in the manuscript were calculated using the 2.5th and 97.5th percentiles from the Monte Carlo simulations, corresponding to a 95% confidence interval. We have corrected Sect. 2.4 accordingly and ensured that the confidence interval terminology is now consistent throughout the manuscript.
Citation: https://doi.org/10.5194/essd-2026-218-CC6
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CC6: 'Reply on CC4', Xiaoyu Zhang, 16 Jun 2026
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CC5: 'Reply on CC3', Fengming Xi, 05 Jun 2026
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- The manuscript involves many abbreviations and symbols in the equations; it is better to provide a list of abbreviations to let the readers easy to look up.
Author Response: Thank you for this helpful suggestion. We have added a new table entitled “Abbreviations and symbols” after the Abstract. The table lists the main abbreviations and equation symbols used in the manuscript, including BFS, CEF, CS, GCB, IPPU, LKD, LM, LSS, MOR, PCC, RM, ROW, SS, SUG.
- Line 262-264: “Beyond the production stage, nine industrial byproducts and materials are incorporated across three sectors: metallurgy (SS, BFS, RM), chemicals (PCC, CS, SUG, LM), and construction (LSS, MOR)”. Why focusing on these nine industrial byproducts and materials? Do you have a reference to support the products selection?
Author Response: We thank for this helpful comment. We have revised Sect. 2.3 to clarify the basis for selecting these materials. The material boundary of this study was developed based on the lime carbon sink framework proposed by Liu et al. (2018), who reviewed lime carbon sinks from the perspective of material flow analysis and identified the main lime-related carbonation pathways in the chemical, metallurgical, construction, and lime kiln dust treatment sectors. Building on this framework, our study retained the major lime-based material flows and newly incorporated blast furnace slag (BFS) to address the previously omitted metallurgical carbonation sink. We have added Liu et al. (2018) as a supporting reference and clarified this rationale in the revised manuscript.
- The format of equations should to be consistent. For example, Line 255 is (3), while others are labeled as “Eq (4)”. Also, there are many equations appears in the text, and not numbers. For example, an equation in Line 300 and all equations in section 2.3.2. Please double check the formatting requirement of ESSD and revise them accordingly.
Author Response: We thank for this helpful comment. We have carefully checked the equation formatting throughout the manuscript and revised it according to the ESSD formatting requirements. All displayed equations are now numbered consecutively using the format (n), and in-text references to equations are consistently written as Eq. (n). In addition, the equations that were previously embedded in the text, especially those in Sect. 2.3.2, have been converted into displayed and numbered equations to improve clarity and consistency.
- There are several typo errors, such as “CO2”should be “CO2”.
Author Response: We thank the reviewer for pointing this out. We have carefully checked the manuscript and corrected the typographical and formatting errors throughout the text. In particular, CO2 has been revised to CO₂, and other chemical formulas and minor formatting issues have also been standardized as required.
- Section 3.4: Strengthen the mechanism explanation of the time lag effect in the discussion to clarify why historical carbon sequestration keeps rising.
Author Response: We thank for this helpful suggestion. We have revised Sect. 3.4 to strengthen the mechanistic explanation of the time-lag effect. Specifically, we clarified that annual CO₂ uptake is contributed by both newly produced lime-based materials and legacy materials produced in earlier years. Fast-carbonating materials mainly contribute to current-year uptake, whereas MOR, LM, RM, SS, and BFS follow multi-year carbonation process. We further explained that the formation of a CaCO₃ product layer slows inward CO₂ diffusion, causing carbonation to shift from rapid initial uptake to a slower diffusion-controlled stage. Therefore, each production year adds a new layer of slowly carbonating materials, while earlier layers continue to absorb CO₂ until their reactive Ca-bearing phases are progressively depleted. This explains why historical carbon uptake keeps rising.
- It is recommended to add “future work” to illustrate how to make the research better. For example, future scenario projections (2025–2050) will to studied to support global and national carbon neutrality goals.
Author Response: We thank for this valuable suggestion. We have added a future-work paragraph to the Conclusion. The revised text states that future work should extend the historical dataset to 2025–2050 scenario projections and consider changes in lime demand, regional industrial structure, kiln technology, material recycling, slag utilization, CCUS deployment, and enhanced-carbonation pathways. This addition clarifies how the dataset can be further developed to support global and national carbon-neutrality assessments.
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RC2: 'Comment on essd-2026-218', Anonymous Referee #2, 08 Jun 2026
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General Assessment
This manuscript addresses an important gap in long-term carbon budget assessments and emission inventories by providing a relatively detailed quantification of process emissions and carbonation sinks associated with lime production. The study is potentially valuable and relevant to the ESSD community, as it contributes to improving our understanding of an often overlooked source of anthropogenic carbon emissions.
However, before publication, a number of important issues need to be addressed. In particular, the methodological framework is difficult to follow, and several key assumptions and parameters require much clearer justification and validation. My major concerns are summarized below.
Major Comments
1. Positioning relative to previous carbon inventories
In the Introduction, the authors should more clearly describe how lime production has been treated in previous carbon emission inventories and carbon budget assessments. Some inventories may only account for cement-related emissions, while others at least report emissions from lime production. The authors should provide a more comprehensive review and discussion of existing treatments in major inventories, including but not limited to
Global Carbon Budget (GCB): https://essd.copernicus.org/articles/18/3211/2026/
EDGAR: https://essd.copernicus.org/articles/16/2811/2024/
MEIC: https://link.springer.com/article/10.1007/s11430-023-1230-3
CDIAC: https://essd.copernicus.org/articles/13/1667/2021/essd-13-1667-2021.html
, and clearly explain what gap remains to be filled by the present study.
2. Clarification of the “verified dataset” terminology
The manuscript repeatedly refers to a “verified dataset” and cites the authors’ previous work. However, it remains unclear what “verified” specifically means in this context. What metrics were used for verification? What independent data sources were used? Why should readers consider these datasets to be more reliable than alternative sources? Based on my understanding, true verification of historical lime production statistics is challenging. Therefore, the authors should provide a much more explicit explanation of the verification procedure and the associated limitations.
3. Integration of multiple activity data sources
The activity data are compiled from numerous sources. I understand that no single data source exists for such a long historical period, making data integration necessary. However, different sources often have substantial differences in statistical boundaries, collection methodologies, reporting practices, and data quality. Ensuring temporal consistency and comparability is therefore a major challenge. Even within a single source such as USGS, data quality may vary considerably over time.
The manuscript currently does not adequately explain how these issues were addressed. I strongly recommend that the authors provide a concrete example illustrating the process of harmonizing and merging different data sources. In addition, uncertainties arising from data integration should be explicitly reflected in the uncertainty analysis.
4. Improving the transparency of the methodological framework
The current methodology is difficult to follow because it combines multiple dimensions, including country, industrial sector, and material type. I suggest adding a summary table that clearly documents which data sources and models are used for each country, sector, and material category. Such a table could also include a qualitative uncertainty rating (e.g. A, B, C, and D). This would substantially improve the transparency and reproducibility of the dataset.
5. Potential underestimation of uncertainty
The uncertainty ranges shown in Figure 5 appear unrealistically small given the complexity of the underlying data and assumptions. This may partly result from overly narrow probability distributions assigned to key parameters, but it also appears that several important sources of uncertainty have not been considered.
For example, different data sources likely have different levels of reliability; original data and gap-filled data should not be assigned identical uncertainties; and uncertainty levels are expected to vary substantially across countries. These factors do not appear to be adequately represented in the current uncertainty framework. The authors should revisit the uncertainty analysis and provide stronger justification for the reported confidence intervals. It would be good if country-level uncertainties are shown in Fig. 6.
6. Suspicious temporal signals potentially caused by data inconsistencies
Related to the previous concern, Figure 6 exhibits several unusual temporal features that appear difficult to explain physically. Examples include the sharp decline in Italy around 1980, the sharp decline in Australia around 2010, and the abrupt increase in Brazil around 1980. These features appear unnatural and may be artifacts introduced by inconsistencies among data sources.
The authors should carefully investigate these anomalies, improve the harmonization of different data sources where necessary, or provide convincing explanations for the observed patterns.
Minor Comments
1. Lines 54–57: The statement appears to be incorrect. It is unlikely that 65% of global emissions originate from limestone calcination. Please verify the calculation and revise the text accordingly.
2. Line 180: The manuscript states that a linear regression model was used. However, the rationale for selecting this model, the data used for calibration, and the validation procedure are not adequately described.
3. Line 220: The authors state that they “calculated” emission factors. Additional information should be provided regarding the methodology and underlying assumptions.
4. The manuscript frequently reports values to two decimal places. Given the substantial uncertainties involved, one decimal place would likely be sufficient in most cases.
5. Figure 2: The legend appears to contain a typo. “Progress emissions” may not be the intended term.
6. Please maintain a consistent color scheme throughout the manuscript. For example, Figures 2 and 3 appear to represent the same regional classification but use different colors, which makes it difficult for readers to follow the discussion.
Citation: https://doi.org/10.5194/essd-2026-218-RC2
Data sets
Global and National CO2 Emission from Lime Production Process and Carbonation sink from 1930 to 2024 L. Bing et al. https://zenodo.org/records/18616060
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The study provides a comprehensive assessment of CO2emissions from lime production and carbonation sinks, establishing both global and national database covering the period 1930-2024. It offers valuable contributions to industrial carbon cycle research and data sharing. The study quantifies key national-level lime emissions at Tier 2 method, which improves the robustness of lime process carbon accounting. It further elevates lime carbonation to the level of global carbon balance and carbon budget discussions, supporting the integration of limecarbonation sink into broader carbon cycle. In addition, the inclusion of carbonation sink from blast furnace slag further strengthens the relevance of the study to industrial symbiosis and metallurgical carbon sink research. Overall, the dataset is comprehensive in temporal and spatial coverage, and the methodological framework is well developed, aligning well with the scope of ESSD as a high-quality data descriptor paper.
The following issues should be further clarified by the authors: