Scientific literature on carbon dioxide removal revealed as much larger through AI-enhanced systematic mapping

Domain: earth_science
Published: 2025-07-18
Editorial outcome: major_revision

Abstract
Carbon dioxide removal plays an important role in any strategy to limit global warming to well below 2°C. Keeping abreast with the scientific evidence using rigorous evidence synthesis methods is an important prerequisite for sustainably scaling these methods. Here, we use artificial intelligence to provide a comprehensive systematic map of carbon dioxide removal research. We find a total of 28,976 studies on carbon dioxide removal—3–4 times more than previously suggested. Growth in research is faster than for the field of climate change research as a whole, but very concentrated in specific areas—such as biochar, certain research methods like lab and field experiments, and particular regions like China. Patterns of carbon dioxide removal research contrast with trends in patenting and deployment, highlighting the differing development stages of these technologies. As carbon dioxide removal gains importance for the Paris climate goals, our systematic map can support rigorous evidence synthesis for the IPCC and other assessments.

Introduction
To comply with the Paris agreement and to limit global warming well below 2°C, rapid and deep GHG emissions reductions, need to be complemented with Carbon Dioxide Removal (CDR), potentially at the gigaton scale by the mid-century and beyond1,2.

CDR has three distinct roles in climate change mitigation1: first, to reduce net CO₂ and greenhouse gas emissions in the near term, specifically in the land sector; second, to offset residual emissions from “hard to mitigate” sectors like industry, long-distance transport, and agriculture in the medium term3,4; and third, to support sustained net-negative emissions in the long term, helping to lower global temperatures in overshoot scenarios and stabilising warming at or below 1.5°C1. Of course, CDR cannot compensate for stringent emission reductions, which need to be prioritised even in hard to mitigate sectors5. There are also deep uncertainties with respect to how fast CDR can be sustainably scaled-up, and whether the reversal of temperature overshoot can be safely achieved6. This underlines the need to reduce emissions as fast as possible, while providing sufficient policy support that CDR can actually deliver gigatons of removals in the second half of the 21st century2,7.

CDR has been a key part of climate change mitigation discussions in the scientific literature, but has often been separated into distinct knowledge domains. A stream of literature going back to the first IPCC assessment reports has considered the potential contributions of enhanced natural sinks through afforestation or soil carbon sequestration to achieve net emissions reductions8,9. This area has since broadened to include analogous nature-based approaches in other ecosystems, such as coastal blue carbon10, alongside options aimed at enhancing ecosystems’ ability to absorb and store CO₂, like ocean fertilisation and macroalgae afforestation11,12. Bioenergy with carbon capture and storage (BECCS) technologies have gained prominence in the early 2010s as an explicit option for achieving negative emissions in the integrated assessment modelling (IAM) literature13–15; while a range of other technologies such as biochar produced by pyrolysis, direct air carbon capture and storage (DACCS), enhanced weathering (EW), and ocean-based approaches such as ocean alkalinity enhancement (OAE) are now gaining more scientific attention15–17.

In the policy domain, CDR has gained increasing attention in recent years18,19, but many countries still lack concrete policies to scale CDR2. This has led to a considerable gap between countries’ (so far limited) plans to develop and deploy CDR versus CDR’s estimated role in mitigation scenarios that stabilise global temperatures at an increase well below 2°C2,19–21. One of the challenges here is that there is a large spread of possible CDR levels that countries might aim for, in part driven by model assumptions of technological innovation and potential market adoption22.

In the age of big literature23,24—where the scientific literature grows at increasing rates—balancing a research question’s scope and the resource demands for reviews, like reviewer time, is increasingly challenging25. To address this issue, systematic mapping methodologies (systematic maps, evidence gap maps, etc.) have been developed by the evidence synthesis community26,27, to map existing literature, identify knowledge gaps and clusters, and guide where reviews are most beneficial. However, these methods remain resource-intensive, prompting discussions about the prospects of automation28,29. Proposals for implementing such automated synthesis approaches have been developed across various scopes and scales23,30–32.

There is currently little systematic oversight of the available CDR literature. As the IPCC’s 7th Assessment Cycle is starting and CDR-related policies and targets are being established, it is timely to assess the current landscape of evidence for CDR. Previous research suggests that there is a large and fast-growing evidence base on CDR, but the few available overviews of the field have rapidly become outdated33,34, only give a coarse overview2,35 or are limited in scope by manual efforts supported by community-crowdsourcing36.

The diverse range of CDR options and multidisciplinary fields involved in CDR research also adds to the complexity of this task, as researchers from different disciplines, each with their own specialised languages and methodologies, may be working on the same issues without fully knowing or engaging with each other due to misaligned terminology. It is also crucial to identify and keep track of gaps in the literature in order to effectively allocate research resources.

Here, we follow a systematic mapping methodology to comprehensively layout the body of knowledge on CDR. We ask an open-framed question—“what is the available evidence on CDR?”—and follow a robust, step-wise methodological procedure that ensures transparency, comprehensiveness and reproducibility. Traditionally, systematic maps have been compiled manually and therefore are often limited in scope. Here, we use an approach that deploys machine learning methods to automate labour intensive tasks to provide an assessment at scale. By doing so, we are not only able to quantify the size and scope of the research landscape of CDR and its temporal dynamics, but are also able to assess the distribution of research efforts across various dimensions, including CDR options, research methodology, disciplinary structure and geographic focus. Furthermore, our machine-learning approach enables swift updating of the dataset in the future.

Given the growing importance of CDR in the context of net-zero strategies and temperature overshoot, our publicly available database of CDR research will be of benefit to the research community as well as upcoming scientific assessments of CDR.

In this article, we first quantify the total volume of CDR literature and examine its temporal trends, as well as dissecting the literature by individual CDR options to highlight shifts in research focus. Next, we investigate the origins of these studies, exploring regional profiles and analysing research that specifies geographic locations to identify patterns in CDR research distribution. We then assess the focus of the studies, including the scientific methods employed, to understand how research approaches have evolved. Additionally, we evaluate the representation of CDR literature in the recent IPCC report, comparing it to the overall CDR literature to highlight any discrepancies. Finally, we compare the attention given to different CDR options across various contexts, including Integrated Assessment Model (IAM) scenarios, deployment strategies, and investment patterns.

Results
Literature on CDR is much larger than previously estimated
There is a much larger body of CDR research than previously suggested. Based on our machine learning assisted approach that enables us to identify CDR studies with high precision (0.88 ± 0.0119, meaning the proportion of relevant studies among those identified is high) and recall (0.93 ± 0.005, indicating most relevant studies are captured)—see “Methods”, Supplementary Methods 3 and 4 and Figs. 1 and 2—we predict a total of 28,976 ± 3800 scientific studies in the Web of Science and Scopus (the two largest bibliographic core collections). This is 3–4 times larger than what previous scientometric studies33 or ongoing community efforts to manually track CDR research36 have suggested when comparing the same time range. For the former study, this discrepancy likely arises from their reliance on non-machine learning methods, which forced a high-precision, low-recall search approach. In the case of the manual tracking efforts, the rapid expansion of CDR literature has simply made comprehensive tracking unfeasible.

CDR research today comprises only 5% of the overall scientific literature on climate change23, but growth in CDR research is faster than for climate overall. We observe an average annual growth rate of 17% over the past ten years compared to a 12% growth rate for the literature on climate change (Fig. 3).

Patterns of CDR research are uneven and dominated by biochar studies
The distribution of research across different CDR options is highly concentrated on biochar and land-based methods such as soil carbon sequestration and afforestation/reforestation. Most growth rates of the different CDR options are generally higher compared to the climate change literature as a whole (Fig. 3 and Supplementary Table 1).

Biochar research is covered in 56% of the 28,976 scientific publications on CDR. Considering 2022 only, the share of publications covering biochar even increases to 62%.

With an annual growth rate of 18% in the past 5 years and its large number of publications, biochar is the main driver of the high growth rate of the entire CDR literature. The second largest category, SCS, with 24% of the total literature, is also growing fast at 14% per year in the past 5 years.

Other biological CDR options make up a sizable amount of the CDR literature, such as afforestation/reforestation with 12% of all studies, agroforestry with 9.7% of all studies, coastal wetland (blue carbon) management with 4.7% and landscape restoration, such as peatlands with 3.5%. Growth rates in the past 5 years are generally in the range of 20–22%, except for the long established CDR options such as afforestation/reforestation and forest management with 14% and 15% respectively. Growth in newly emerging research areas tends to be particularly high, as initial literature numbers are low, making each new publication a relatively larger addition to the existing body of work.

BECCS is represented in only 5.6% of all studies on CDR, despite being the most common CDR option in most scenario pathways for meeting the Paris temperature goal37–39. DACCS accounts for only 2.8% of all CDR studies. The annual growth rate of BECCS is volatile and the average of the past 5 years is 12%.

Other CDR options are much less represented in the scientific literature: for ocean fertilisation, EW and OAE, we found less than 50 studies for each option per year.

CDR research is concentrated in China and OECD countries
We use the first author affiliation to infer the origin of the studies. This approach simplifies the complexities of international collaborations, where authorship, lead roles, and funding often span multiple countries. However, we consider it a valuable proxy for drawing meaningful conclusions about the research origins. With our approach, we find that China is responsible for the largest amount of research on CDR with 6452 studies (30% of all studies where author affiliation is available), followed by the United States (2667 studies, 13%) and the United Kingdom (953 studies, 4%). Only 3.4% of all studies with author affiliation have a first author affiliation from South America and 2.8% come from Africa, see Supplementary Fig. 6.

Though global research trends favour land-based CDR options, specialisation varies. China focuses more on biochar research, Europe on BECCS, and North America, particularly the US, on DACCS in comparison to the average shares per CDR option across all countries. Research on ocean-based CDR options is more predominant in Oceania and North America (Fig. 3). We provide detailed country profiles for CDR research in the Supplementary Fig. 5.

Roughly one third of CDR research refers to specific geographic locations, identified through named entity recognition in titles and abstracts40. Place-based research is important for evaluating CDR implementation in situ, including aspects such as aspects such as effectiveness of removing CO2 or environmental or social side-effects. Out of 28,976 studies, 9305 mention a location, of which 74% are countries and 25% are sub-national allocations such as federated states, counties or cities. Soil and vegetation-based CDR options feature more place-specific research, with afforestation/reforestation studies at 65%, compared to 33% for enhanced rock weathering, and only 10% for DAC(CS) research, Fig. 4. Further information on regional based research with details to the specific regions can be found in the Supplementary Figs. 7 and 8.

CDR literature focuses on technology research using experimental methods and modelling
Our ML approach further enabled us to classify CDR research contents along key dimensions. In particular, we used our classifiers to distinguish research methods and the broad area of research. Additionally, we used the journal, in which a publication appeared, to determine academic disciplines in line with the relevant OECD Category scheme41.

We refer to CDR research that aims to understand, design or further develop CDR options, their efficiency and side-effects as “technology research”. As indicated by Supplementary Fig. 10, technology research accounts for about 89% of all studies across all individual CDR options. We refer to survey or focus group research on public perceptions and attitudes to CDR as “public perception” (0.8% of the total), and integrated assessment scenario research as “socio-economic pathways” (9% of the total). We further classify “policy and governance” research, and studies on the “earth system” that evaluate global carbon cycle or land aspects of CDR implementation, see for example42,43, even though these categories remain relatively rare (at 3.8% and 0.6%, respectively).

In general, patterns of research vary across the technology categories. The literature on CDR in general features mainly policy and governance (28% of papers on CDR in the general category) as well as integrated scenario research (45%). We also find larger shares of scenario research for some individual CDR options—particularly for BECCS (31%) and forest-related CDR options (21–22% for Forest Management and Afforestation/Reforestation), which were the first to be implemented in the modelling community38,44.

CDR research is published to a large extent in journals with a natural science or engineering focus and tends to be rooted in experimental and modelling study designs. In particular, 50% of the studies are published in natural sciences, 26% in agricultural sciences and 22% in engineering and technology journals (see Fig. 5). Only 3% of the publications are published in journals with a focus on social science, including economics.

Research designs vary substantially across CDR options, but experiments, reviews and modelling studies are most common. Overall, 86% use experimental methods, either laboratory (48%) or field (38%) experiments, driven mainly by research on biochar and soil carbon sequestration. Reviews (21%) and modelling (18%) make up another large proportion. However, certain research designs are more dominant in the literature for specific CDR options. For example, field studies typically make up a substantial share of forest-based CDR options, but also blue carbon and ocean fertilisation, while laboratory experiments, i.e., experiments in a controlled environment45, are dominant for biochar, soil carbon sequestration, but also some engineered CDR options such as DACCS. Interestingly, BECCS studies to date focus strongly on modelling, highlighting their prominent role in climate protection scenario work. Across all CDR options, reviews are widely available—from 11% for forest management up to 32% for OAE, and 34% for the literature on CDR in general (Fig. 4).

IPCC reports differ greatly from scientific literature
Next, we analyse how the research landscape is reflected in the most recent 6th Assessment Report by the IPCC. For this, we extract all citations from the IPCC AR6, all working groups, and identify those studies which are present in the literature on CDR by matching titles. Although it is clear that the IPCC cannot assess all of the large and growing body of available research33, it is essential to understand which main topics are emphasised or overlooked. We also acknowledge that differences between the two literature bodies can arise from various factors and that the main topic distributions should not necessarily align—as Hume remarked, what “is” doesn’t necessarily lead us to what “ought to be”.

We find that IPCC assessments are not a broad reflection of attention patterns in the underlying scientific literature on CDR options (Fig. 6). Overall, only a small fraction (2% of the CDR literature) of CDR studies are directly assessed. While the IPCC includes a relatively higher proportion of reviews (19% vs. 15%) and systematic reviews (3% vs. 1%) compared to the overall CDR literature, we believe incorporating even more of these could further enhance its ability to fulfil a stated goal of the IPCC—which is to comprehensively evaluate the available evidence.

IPCC assessments cite a wider range of CDR options, but are predominantly concerned with BECCS (27%)—probably due to its prominence in climate change mitigation scenarios (see Fig. 6)2,46. The major focus on biochar in the research community is not reflected in IPCC citation patterns.

The fact that IPCC assessments have tended to focus on scenarios is underlined by an observed shift from experimental research (86% of CDR research; 10% of IPCC citations) to modelling work (13% of all CDR research; 37% in IPCC citations) and data analysis (10% of all CDR research, 19% in IPCC citations). The focus on technology research in the literature (89% of all CDR research; 44% in IPCC citations) is replaced by much more prominence of scenario work (socio-economic pathways) (9% of all CDR research; 33% in IPCC citations) as well as research on policy and governance (4% of all CDR research; 22% in IPCC citations). All this reflects that IPCC assessments focus on the exploration of alternative scenarios with different climate outcomes, societal development pathways and mixes of mitigation strategies, intended to inform policy development33,47,48.

Shares of CDR options vary across indicators of policy and practice
Finally, we find that the CDR options being researched most intensively are not the ones being most actively deployed, developed or invested in (Fig. 7, ref. 2, Chapter 3,6,7). Again, we do not imply that these distributions should necessarily be similar; rather, we aim to highlight and reflect on the differences between these categories. For example, while CDR research strongly focuses on biochar and soil carbon sequestration, the vast majority of current deployment (2Gt yr-1 or 99.9%49) is from afforestation and reforestation. Conversely, even though only 2Mt yr-1 of CO2 removal is currently delivered by more novel CDR options—mainly BECCS (78%) and biochar (21%)2 —these technologies receive an enormous amount of scientific attention or are widely discussed in the scenario literature. Similarly, about 80% of the CDR patents are for BECCS and DACCS2,50. 75% of announced investments in CDR focus on DACCS projects51. In long-term mitigation scenarios that achieve the Paris long-term temperature goals52 mainly BECCS (99%), afforestation (67%) and DACCS (29%) are the CDR options included. There is not a single scenario dealing with biochar or soil carbon sequestration due to a lack of implementation of these CDR options despite their potential co-benefits, such as food security or N2O emission reduction53.

Methods
Systematic map-protocol
We use an approach assisted by machine learning to provide the a comprehensive evidence map of CDR research. We follow the well established guidelines for systematic mapping25, wherever possible, and adjust them as needed to align with our machine learning approach. We document all steps in a detailed systematic map protocol for transparency and reproducibility45, which is summarised in Fig. 1 and Supplementary Fig. 11.

Document search
We started by developing, for each CDR option, search strings with high levels of recall to make sure that as few scientific articles are missed as possible. The search strings include keywords describing the CDR technology, see Supplementary Information for the full search queries. For long established CDR options, such as afforestation, we included keywords that make sure the CDR option is evaluated with a focus on carbon sequestration. The development of search strings was done iteratively by validating against an independent list of publications on the various CDR options ensuring that all documents are returned. The validation dataset was extracted from IPCC AR659,60 and 50 randomly selected publications from the CDR bibliography36 published by the Climate Protection and Restoration Initiative. The search queries are available in the Supplementary Table 3. We then ran the final search strings on Web of Science and Scopus on March 28th, 2022 and May 3rd, 2023 and retrieved 75,518 bibliographic records after de-deduplication. Further information on this procedure and information on the validation dataset is available in Supplementary Table 1 and Supplementary Method 1.

Document relevance through machine learning
In the next step, we work towards precision by developing a machine-learning classifier to distinguish relevant, namely all studies on negative emissions and CDR, from irrelevant scientific studies in our query. We manually screen and annotate a total of 5339 documents— 100–600 per CDR option—if they should be included in the map (distinction between blue and red squares in Fig. 1) according to our codebook. To ensure reproducibility61,62, each document is screened and annotated by two coders as recommended by the relevant guidelines25. We use our annotations to train and validate binary classifiers, i.e., automatic sorting into predefined categories, to predict inclusion, using the title and abstract of the documents as inputs. The best performing classifier (F1: 0.91; ROC-AUC: 0.85) is derived from ClimateBERT—a transformer-based pre-trained language model, which has been fine-tuned to better represent domain-specific language used in the climate change context, including in scientific abstracts63. Further details and an explanation of our model validation procedure are available in the Supplementary Methods 2 and 3.

Document classification through machine learning
We further annotated all relevant scientific articles from our manually coded training and validation set with regard to the CDR options covered (Afforestation/Reforestation, Restoration of landscapes/peats, Agroforestry, Soil Carbon Sequestration (SCS), Blue Carbon Management (mangroves, macroalgae, seagrasses, and salt marshes), EW, OAE, Ocean Fertilisation/Artificial Upwelling, Bioenergy Carbon Capture and Sequestration (BECCS), Direct Air Carbon Capture and Sequestration (DACCS), Biochar, additionally we include General Literature on CDR with no focus on a specific technology), the scientific method used, as well as the broad area of research (technology study, policy & governance, equity, public perception, socio-economic scenarios, earth system science). Definitions of all CDR methods used to code the documents are shown in Fig. 2. Additional information on how we distinguished the different classes can be found in our coding protocol45. The additional categories are represented in Fig. 1 by the different blue shades for each annotated relevant document.

We used these annotations to train three multi-label classifiers for second stage predictions, and apply them to documents predicted relevant at the first stage. We achieve Macro F1/Macro ROC AUC scores 0.77/0.87 for the “technology” classifier, 0.69/0.89 for the “methodology” classifier and 0.62/0.77 for the main “area of research” classifier.

Machine learning validation
Throughout this process, we evaluate and validate our methodological choices. We test our ClimateBERT classifications against classifications from DistilBERT64 as well as a much simpler classification approach, where we use tf-idf-encoding together with an SDGClassifier with Huber-loss65. ClimateBERT is chosen here due to its better performance (see Supplementary Table 3). We optimise classifier performance by tuning the hyperparameters of our model using the Python package RayTune66. Finally, we test the complete training strategy of all classifiers in a threefold cross validation providing us with comprehensive estimates of how the classifiers perform on the complete dataset (cf. Supplementary Table 4–6). To estimate the confidence intervals for absolute counts, we estimated the True Positive Rate and False Positive Rate from our validation procedure and calculated their confidence intervals using binomial proportion confidence intervals, see Supplementary Method 4.

Locations in title and abstract
To find the locations in title and abstract, we deployed the Python package Mordecai40.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This paper contributes a novel machine learning-assisted systematic mapping methodology to provide a comprehensive evidence map of the CDR literature. The manuscript is well-written and motivated. The written language is simple and easy to be read for people of a different background The novelty of the study is also presented clearly and the systematic mapping approach is comprehensively presented in the supporting documents. Overall, a very useful and very much needed tool. With some additional filters this tool has the potential to provide a great service to the CDR community and climate change mitigation efforts in the wider context.\n\nHowever, I miss a reflection on the potential reason for certain results, which would be helpful for further guidance. Also, the discussion could benefit from clear recommendations for the CDR community on where to focus on (and why?).\n\nAlso, it would be great to see a progression of the tool to more specificity (which might have been outside the scope of this work). For example:\n• For BECCS, further coding based on biomass origin, for DACCS, further coding based on type of sorbent/solvent used, etc. This would allow an overview of the CDR literature space not only based on macro-CDR methods, but going more into the specific technology (e.g., high-temperature vs. low-temperature DACCS).\n• For LCA studies categorize the function of the system (heat generation, biofuels production, application to soil, co2 sequestration), which would help in refining the literature landscape overview.\n\nMain comments:\n• “In policy, CDR has gained increasing attention in recent years (Geden, Scott, and Palmer 2018; Schenuit et al. 2021), but many countries still lack concrete policies to scale CDR (Smith, S. M. et al. 2023) Chapter 5). This has led to a considerable gap between countries’ (so far limited) plans to develop and deploy CDR versus CDR’s required role in mitigation scenarios that stabilise global temperatures at an increase well below 2°C. (Bellamy 2018; Cox and Edwards 2019; Schenuit et al. 2021; Smith, S. M. et al. 2023). (page 2, main)\no I would specify that the quantitative information of CDR required in mitigation scenarios is obtained from model-based scenario analyses. Hence it is a number resulting from modeling assumption and model “fingerprints.” I would find it important to specify that there is still a large spread of uncertainty associated with the scenario output: 1) Structural uncertainty (regarding technological innovation and uptake, market behaviour, etc. Assumptions in the models), 2) Parametric uncertainties (involving differences in parameter calibrations, regional and temporal scale) and 3) Fundamental modelling choices (e.g., optimization versus simulation frameworks), model structure and foresight. (Dekker et al., 2023, Nature Energy)\n• In Figure 3, it would be interesting to see, for the location-based research the correlations between CDR methods and the specific geographic locations studied. Is there a difference between this map and the results of Fig. 3a-3b?\n• In long-term mitigation scenarios that achieve the Paris long-term temperature goals (Byers et al. 2022) mainly BECCS (99%), afforestation (67%) and DACCS (29%) are the CDR options included. There is not a single scenario dealing with biochar or soil carbon sequestration, although these methods can deliver co-benefits such as food security or N2O emission reduction (Lehmann et al. 2021).\no Can we contextualize this statement more? Do the models not include biochar and soil carbon sequestration in their representation (i.e., the reason why these methods do not appear in the scenarios), or are they represented, but the models do not choose these methods in mitigation pathways (if so, why)?\n• Coding Framework: Long-term storage is defined as at least 5 years. As 5-years is not a climate-relevant time horizon, why wasn’t a longer time horizon chosen in the definition of long-term storage when screening for CDR literature?\n• Figure 1 should be better explained. For example, the figure is not clearly explained in the text or caption (I know the methods are at the end in NC but this is not easy to follow and read to have this figure here). For example, it is unclear why and how documents are coded by hand. Why is each document coded by two people, does this mean all 30,000 papers?\n• The authors report the results consistently, however, I would be interested for the reason of reported results, e.g. why some CDR methods are more popular in certain regions. I would suggest to elaborate on this in either the results or the discussion. For example;\n‘China focuses more on biochar research, Europe on BECCS, and North America,\nparticularly the US, on DACCS relative to the research shares per technology in all\ncountries. Research on ocean-based CDR methods is more predominant in Oceania and\nNorth America’\nCan the authors give reasons why (in China)? For example, certain regimes, policies implemented, social pressure/acceptance? This would be useful to better understand how certain aspects can impact research efforts.\n• I also do think that the results can be better structured in terms of paragraphs. Please think about the key messages you want to provide. For example, in section 2.2, the authors are going a bit back and forth between the number of studies of CDR options while I don’t think that for each method one has to provide the results using its relative share of overall number of studies.\n• If possible, please provide the search terms in the methods or SI.\n• Maybe I missed it, but why is storing CO2 in wooden buildings or concrete not considered as CDR option?\n• On the one hand, I liked the benchmarking of the research landscape with regard to the IPCC report. On the other hand, I am not sure how useful it is, i.e. I am not sure whether the IPCC should reflect the overall underlying scientific literature or instead should just choose the most relevant studies.\n• The discussion/conclusions seem more like a repetition of the results. They should be partly revised by provided a set of recommendations for the CDR community.\n• It would be great if you mention somewhere in the paper, what makes this tool so useful and be specific about it. Highlight the added value of using machine learning but also the novelty aspect of your work.\nAbout the tool: If possible, it would be great to have some additional filters in the tool itself;\n1. Filtering the papers by research gap would make a major difference and position this tool above any existing online tool used for literature mapping as of today. It would probably mean a lot of work but think about the functionality of your tool. This would in turn increase its usability and expose your work widely in the research community and beyond. In addition to this, it would also be very interesting to have a discussion in the paper around what are the major research gaps being addressed as of today.\n2. In the LCA studies, one of the most important elements when putting together the study is the functional unit of the assessment which connects to the function of the system being analysed. For example, if I am looking at an LCA study of biochar, the system at hand could focus on the environmental impacts of biochar production. Other functions biochar is used for concerns: a) energy production, b) biofuel production c) biochar application to soil for carbon removal d) biochar used for soil purification etc. My point is, it is of crucial importance that you add another layer of filter where the LCA studies are filtered by the function of the system. For example, as of now the tool results in roughly 200 LCA studies on biochar but If I want to look only the studies on biochar application to soil, I will not get more than 6 to 10 LCAs. This would be a major innovative element of your tool.\n3. In the same line as the LCA part, it is very important to provide a filter that specifies the IAMs scenarios used for the studies that work with them. And this is another point that can be discussed in your paper.\n4. Add another filter concerning the Technological readiness level (TRL) of the CDRs. In a way this could as well explain why there is a certain focus on specific CDRs compared to some others. In terms of functionality this would be very useful for different stakeholders beyond research (investors, governments) who want to learn mainly about the technologies that are ready for large scale implementation.\nOther minor comments:\n• Fig. 3 caption: “right” and “left” panel are inverted in the caption text\n• Data and Methods section, page 15, first paragraph: SI-Figure 5 instead of SI-Figure 4\n• Supplementary figures and numbered incorrectly:\no E.g., SI Figure 1, related to Figure 3; instead of Figure 2\n• Can we not use machine learning in the title instead of using artificial intelligence, in the methods machine learning is used while in the title AI (I am aware that machine learning is a subset of AI though).\n• I would refer to the online tool available on the web, somewhere in the paper. Is it open source and maintained?\n• I would suggest removing the last paragraph on page 4, the one that summarizes the findings in the introduction section. It is a great paragraph, but you mention these findings in the abstract, discussion and conclusion. I think it would help to reduce any sense of repetitiveness.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "Carbon dioxide removal (CDR) and negative emissions technologies are needed in all scenarios that achieve global warming targets set by the Paris Agreement, and therefore are a crucial consideration for policy makers. This article utilizes emerging applications of artificial intelligence models to generate an automated systematic map of available academic evidence relevant for CDR. This allows the authors to characterize the distribution and extent of academic evidence across relevant metadata variables such as publication year, first author affiliation, intervention type, methodology and scientific discipline. In my view, an important and key contribution of this research is discrepancy that was revealed between the distribution of academic evidence, policy and deployments across different CDR technologies. This finding is particularly timely over the coming decade, as there a strong motivation to scale up CDR (particularly in novel technologies). This systematic map provides valuable insight into knowledge gaps that can inform future research priorities, as well as under-exploited knowledge clusters that can inform CDR investment and deployment.\n\nI want to congratulate the authors on this work, which represents a valuable application of how machine learning tools can be harnessed to conduct evidence synthesis with a large enough scope that is relevant for policy makers. I recommend that this article be accepted for publication, with revision. I have made most of my comments in the attached annotated pdf file, however include more general comments below. In particular, I think the discussion should elaborate more on key findings – specifically how patterns of CDR research differ compared to policy and practice, as well as providing more information and clarity in the methods with respect to the named entity recognition. For all the figures, please verify that the color palettes used are colorblind friendly where possible.\n\nIntroduction\n- Many valuable points are presented in the introduction, however I think a few contours can be better defined that would help to position this work, its motivation and contribution – specifically with how increasing scientific literature interacts with trade offs between scope and available time resources when conducting systematic evidence syntheses, and how automated approaches can be applied in this context.\n- Many different types of interventions are discussed, and the inclusion of a typology figure or table that provides a short description of the main classes could be helpful to clarify the scope of interventions assessed in this map (essentially a summary of the table in the Coding Guideline – Categories – 0 Technology (CDR Technologies). This would also clarify the boundaries between the different types used in Figure 2 – e.g. blue carbon management can be a form/sub-type of soil carbon sequestration? I would suggest including a figure like this and moving table 2 to the supplement\n\nResults\n- More precision/evidence could be added to several statements (e.g. briefly citing F1 statistics, confidence intervals, etc where relevant)\n- Section 2.1 & Figure 2: It seems for all the methods, that annual growth rate starts high and then declines, perhaps because it is easier to grow by a larger percentage when absolute numbers are low. So I wonder how useful a direct comparison is between growth rate for all publications in climate research vs CDR when growth rate seems so affected by the absolute size of the literature base? The overall size of the CDR literature base is given, but what is the size for all publications on climate change, and based on their differences in size, is a direct comparison in annual growth rates suitable?\n\nDiscussion\n- See comments in main text file, particularly my recommendation to elaborate further on the disconnect between policy and practice and scientific knowledge\n\nMethods\n- A note on data and code availability – will the raw data on the articles included in the study, and their predicted relevance for the different metadata variables, be provided along with source code (e.g. in an online repository)? This would be a valuable resource to the CDR community.\n- I did not see detailed in the methods how the named entity recognition in titles and abstracts was performed – could you please specify?", "reviewer_5": "Review report\nSubmitted article: Scientific literature on carbon dioxide removal much larger than previously suggested: insights from an AI-enhanced systematic map\nJournal: Nature Communications\nDate of review: 3/7/2024\nSynopsis\nThe authors predict a much larger literature on CDR than previously found. They use a state-of-the-art supervised machine learning approach, but I am unable to judge if this is a methodical development, or just an advanced, but common, method applied to a new topic, namely CDR. The two central findings of the paper are the large size of the CDR literature and the large focus on biochar. There are four findings of secondary importance: the affiliations of reviewed first authors are concentrated in China and OECD countries; most of the literature is categorized as technology-studies; the reviewed methods are mainly experimental or modelling-based; and the distribution of CDR types in the literature is different from that cited by IPCC and that in policy.\n\nSignificance\nThe application of machine learning is not a major scientific contribution on its own – and nor is it framed as such by the authors. If it was the main contribution, a method-oriented journal may be more fitting. When ML is used in a complementary fashion it can yield valuable insights, but the submitted version is mainly descriptive, and is only an incremental improvement to previous published reviews on CDR. While the authors prediction about the size and the distribution of the CDR literature are important and interesting findings, these are not put into play sufficiently to give further insights. The finding that biochar dominates the literature is interesting, but no empirical effort and almost no theorizing to understand what could explain this dominance. While the two main findings are important and of general interest, I believe they are incremental and that the paper in its current form is lacking an investigation into the results.\n\nRecommendation\nBehind the paper is a substantial effort to screen and annotate a training dataset. The methods are rigorous. The explanation of the machine-learning approach is also easy to follow. The two central findings are of general interest.\n\nThere are two main reasons I recommend a rejection of the paper in its current form.\nFirst, the major issue with clarity renders many arguments in the paper inaccessible. Many basic mistakes (referencing and formatting) coupled with unclear writing and multiple off-hand claims give the impression of insufficient internal editing. While I strive to separate form from content when giving critique, the lack of clarity detracts significantly from the overall quality of the paper. Second, while the two central findings are interesting, there is no investigation into potential underlying explanations. The attempts to contextualize the results are weak and occasionally based on a flawed premise. For instance, the two claims - that citation patterns in IPCC should mirror the distribution of CDR types in the predicted literature and that the distribution of literature point to evidence gaps on certain CDR types – are not convincingly justified. The secondary findings are of limited general interest, and without deeper analysis the central findings remain primarily descriptive.\n\nI encourage the authors to revise the paper thoroughly and resubmit to another journal, since the work contains important results that would be of general interest to the research community, and potentially, as the authors also mention, to the coming work of the IPCC cycle.\n\nNote: While I am confident to provide intermediate judgment, I am unable to provide an expert judgment of the machine learning approach. I have some, but not much, practical experience with applying supervised machine learning and transformer-based language models. From my judgement, the authors’ methods are both rigorous and based in solid experience.\n\nMain points\nThe main argument of the paper is that the CDR literature is much larger than previously thought. This claim could be strengthened by testing whether the algorithm is overly inclusive i.e. predicts too many false positives. I understand the precision of the algorithm is measured on the training data, but further robustness could be checked by sampling some predicted documents. As of now the authors only test that the predicted documents are sufficiently inclusive (validation against a list of IPCC references). There might be advanced ways to do this, but it could also be as simple as extracting a random sample of documents and manually screening how many are relevant.\n\nFigure 1 is well-designed and succeeds in conveying the machine learning approach.\n\nA general issue is the vagueness of what constitutes a CDR study, and the subcategories. For instance, what does it mean that a document is classified as biochar research. The specific in-/exclusion criteria are found in the SI, but a general explanation in the main manuscript would be useful. The many ways in which classification is described leaves the reader unsure “Biochar research is covered in [x] publications”; “Roughly one third of CDR research … refers to …” “there is 3-4 times more research related to CDR” (this sound as if the literature is not mainly on CDR, but something else that is related); “CDR research addresses”; “the second largest category, SCS, with 23% of the total literature”; “BECCS represents only about 5% of all studies” (Side note: BECCS doesn’t represent any studies, it might be represented in some studies though); “the literature on CDR in general features mainly policy”. These examples raise questions of what it means that a study is on a type of CDR such as whether tangential attention sufficient for inclusion, is a mention in the title, abstract and keyword sufficient? What about an IAM-based study that includes, but is not focused on CDR? How many studies contain multiple CDR types? I don’t need answers to these specific questions here, but it would be a great improvement if such questions don’t arise when reading the submission. I also suggest adding a clear definition of what is meant with a CDR study in the methods section, if not in the main. Of course, this can only be a general definition, that goes for all CDR types.\n\nThe amount of predicted CDR literature is compared with previous scientometric studies, but it is unclear if the comparison accounts for the different time of the studies. Further, several of the authors have been part of previous reviews of CDR but found a lot less literature (Minx et al. 2018, Minx et al. 2017) in their search i.e. before screening. Since AI is only used to screen and not to search, it would be of general interest to explain why the initial search (unscreened) results in ~70,000 articles compared to ~6,200 in the 2018 study and 2900 in the 2017 study. Does this review cover more CDR types or were important keywords missing from the previous search queries? How much of the difference is explained by literature published after the previous studies?\n\nThe authors emphasize multiple times that the literature cited by IPCC doesn’t reflect the predicted literature: “IPCC assessments are not a broad reflection of attention patterns in the underlying scientific literature”; “Only 1% of the available scientific evidence on CDR was cited in the IPCC AR6”, and “IPCC assessments cite a much lower share of reviews on CDR relative to the overall literature” (which is followed by the harsh and unfounded claim that this means that “IPPC therefore also fail to indirectly assess the available literature”). This emphasis is repeated for biochar, which dominates the predicted CDR literature, whereas IPCC is dominated by BECCS. However, it is not convincingly explained why IPCC citations should reflect the distribution of studies on each CDR types. In the same vein they write “understanding which subjects [CDR types?] receive more or less attention is vital” – This can be understood simply by looking at the IPCC references study i.e. for this, their mapping is unnecessary. Continuing: “to ensure that IPCC reports continue to provide a balanced assessment of the evidence”. A balanced assessment of evidence connotes a pro-et-contra, but since this make little sense in this context, the reviewer is left to guess what the authors mean; should it be ensured that IPCC continues to cite CDR type proportionally to their distribution in the literature? (Side note: why say continue if IPCC doesn’t reflect it already, as was just claimed). I cannot see why such proportionality is important, but no arguments are given anyway. The authors seem to hold an underlying belief that x number of studies on CDR type A has a weight comparable to an equal number of studies on CDR type B – and therefore that the ratio (or relative number) of studies can indicate where more research is needed (e.g. “can help identify topical areas worthy of focus”; “we find substantial evidence gaps”). I am sceptical to the number of studies can be used in the way done by the authors, but even if so, it is a very noisy signal, and interpretation should therefore be cautious. This critique above also applies to section 2.6, i.e. it is unclear why it is relevant or of value to compare how studies are distributed across CDR types with “other indicators of attention”. If this critique misses the mark in the way that the authors are not claiming that attention patterns should match, then it is still unmotivated why the authors want to compare patterns.\n\nThe article compares the growth of CDR research to the growth of climate change literature, but it is not motivated why this is an interesting comparison, and I am unsure if it is a valid one. Research fields have highly different traditions of publication, e.g. because the cost and scale of experiments differ. I’m sure better ideas exist, but for a start a comparison with the mitigation literature or total scientific output across all disciplines would be improvements.\n\nThe three abovementioned critiques can also be related to David Humes famous “is-ought” problem. This issue was acknowledged in a previous publication by two of the co-authors (Callaghan, Minx & Forster 2020). This issue applies equally to this paper, although there is no acknowledgement of the issue, in this or in other terms.\n\nThe authors make an interesting note in mentioning that high biochar publication rates might be due to its potential co-benefits. Such remarks bring the authors knowledge about CDR to the front and is more relevant to a general journal such as Nature Communications than advanced scientometric methods.\n\nIn paragraph 8, it is argued that by using AI, the authors can assess the “balance of research” (unclear meaning) in terms of CDR types, research methods etc. But this can also be and is often done without AI. With 26 authors and 7 assistants in the acknowledgement, each would have to screen less than 4,500 articles (by titles first and abstracts only where necessary) and annotate less than 1600 documents (double-coding included). These numbers (70,000 and 29,000 divided by the number of co-authors and assistants) can be greatly reduced by first screening by journal subject areas, journal titles, and for irrelevant keywords in journal and document titles. This is not to say that there isn’t a valid motivation for AI, but rather the one stated is insufficient.\n\nGenerally the treatment of CDR is strangely uncritical although CDR is highly contested and politicized (see e.g. Anderson et al. 2023; Carton et al. 2023). Several of the co-authors have already contributed to this debate. To be more specific: The abstract and introduction emphasizes CDR as a critical strategy to limit global warming below 2°C. First, if any strategy is critical, it is emission reductions, whereas CDR is a supplementary strategy (Zickfeld et al 2023). CDR is meaningless if not combined or preceded by emission reductions (Fankhauser et al 2022). The authors mention three roles of CDR. The first role warrants further qualification, since even for residual emissions (hard-to-abate sectors), emission reductions are a preferable strategy to CDR in terms of several indicators (Fuhrman et al 2024). The second role hinges on a misguided fixation on the 2°C goal, rather than the objective of limiting global warming as much as possible. In other words, if emission reductions do not occur rapidly, reductions (and not CDR) will only become more urgent to minimize further global warming – which should be the target of climate change mitigation, rather than expecting CDR can be scaled to match current (i.e. unmitigated) emission levels. This fixation also ignores the expected (and those unknown) detrimental effects of temperature overshoot, even if temporary. The third role of CDR is indeed important, as some CDR options (land-based are mentioned) can lower net emissions and already does so (as mentioned by some of the co-authors in other publications). But any land-based CDR is limited by exactly that – land. In addition, climate change is not the only ecological crisis, which means there are other challenges to large-scale deployment of CDR beside the limited potential for removals. Many CDR options are found to have negative environmental effects (see e.g. Heck et al, 2018; Prütz et al, 2024; Wood-Hansen & van den Bergh 2024).\n\nSpecific minor points\nThe authors mention they find little research on how to build CDR economies where the CDR (…) provides economic revenues beside mitigating global heating. This is a very specific example; an example of such literature would be interesting. It is however unmotivated why CDR should provide economic revenue beside mitigation – this seems a problematic requirement.\n\nWhat is meant with a diverse range of interdisciplinary fields and why does it add to the complexity of reviewing literature? I would think interdisciplinarity would enable reviews, in contrast to unconnected fields.\n\nIt is mentioned that “there is little CDR research on the societal implications [of CDR] such as public acceptance, governance”. The choice of words here is imprecise and unfounded. The mention of societal implications is unclear – nowhere else are these mentioned. The reader is left to speculate if societal implications are understood as a subset of studies predicted to fall in the “context”-classification as “Policy / Government” or “Public perception”, but none of these equal “societal implications”. Note also that public acceptance specifically refers to public perception of policy after implementation (as opposed to acceptability which is ex ante policy adoption).\n\nThe use of the Gini-coefficient to explain the distribution of the literature is unnecessary, as there is no reason to expect an equal distribution.\n\nSection 2.4 refers to Keller et al. 2018 and Werner et al. 2022, but it is unclear what the role of these references is. Are they examples of studies under the label “policy and governance”, or are they meant to support a claim? If the former, please help the reader, for instance by writing, “see e.g.”. The same sentence claims that “policy and governance” and “earth system” are important categories but doesn’t explain why.\n\nThe authors find that the reviewed BECCS studies mainly fall into the “modelling” category and infer that this “points toward limited efforts to research the design of the technology itself”. This inference is invalid, why would an increase of modelling studies using BECCS imply anything about other research efforts into BECCS?\n\nIt is claimed that “There is not a single scenario dealing with biochar or …”, but where does this claim from? Is it a result of the review?\n\np. 13-14 states that the ML-assisted approach enables the authors to be systematic in their procedure, but as they also mention in the introduction there are also systematic reviews not relying on ML.\n\nIt is stated that DACCS is a place-specific CDR-type, but as I understand it, DACCS might be one of the least context-dependent types of CDR, except for the importance of local access to renewable energy? In the same paragraph is the trivial observation that there are fewer studies on novel technologies.\n\nThe authors call for more humanities and social science research to support evidence-based decision making. While I have sympathy for the point, the argument needs sharpening. First, the authors should be aware of the critique of the notion of evidence-based policymaking (see e.g. Head 2010). Second, the humanities do not contribute to evidence-based policymaking, nor is it the ambition.\n\nThe authors suggest using their literature map to identify what has been “under-cited” by the IPCC. This is an interesting idea and sounds potentially useful. It could be an interesting addition to the paper. If not, please elaborate how this idea could be carried out.\n\nThe authors make an unbacked and imprecise claim about what drives political attention (i.e. that relatively slow mitigation action will shift political attention toward CDR). What political attention - the media, the public, or that of political elites? It is entirely speculative that there should be a causal relation between a lack of emission reductions and an increasing attention toward CDR. If we are to speculate, the short-term prospects of economic growth and job creation through technology-exports associated with investments in CDR combined with the hope (among vested interests) that CDR can enable continued fossil fuel extraction are more likely drivers of elite political attention. How close we are to the 2°C goal is probably the least crucial factor in where political attention goes. The political economy of climate change is tightly associated with hegemonies and dominant processes of power i.e. capital accumulation.\n\nPlease provide colourblind-safe versions for the figures in which colour is the only way to distinguish elements. These can be put in the supplementary material. (See the Springer Nature commitment to inclusion)\n\nOn methods\nThe main manuscript is lacking cut-off date for the search queries. The only place I can find it is in a figure in the supplementary material, but it is only given by the month.\n\nIn section 3 “our classification system […] is not only able to predict the relevance”. Does it score of the degree of relevance of each study, or is it a binary sorting into relevant/irrelevant.\n\nSection 2.4 mentions the use of “journal information” without further explanation.\n\nThe difference between the annotation mentioned in paragraph 3 (screen and annotate) and 4 (we further annotate) is unclear.\n\nThe authors claim to achieve high levels of recall and precision. These terms are not my expertise, but I understand recall to be a ratio, but it seems here to be used in the sense of resulting in many search results. A footnote or explanation could be included in the supplementary material. The same goes for precision; it is unclear how it is measured. I understand these may be related to the classifiers and their scores such as F1 and ROC-AUC. Even though this is technical jargon, it can be explained in simple terms in the supplementary material.\n\nThe authors acknowledge that their classification system is not perfect, which is a trivial point. It is then argued that manually compiled evidence maps are assumed to be perfect, which sounds like an unnecessary strawman.\n\nOn form and style\nThe lack of basic formatting in the supplementary materials should not need mentioning. Please provide page numbers in all documents. The lack of formatting in the document headed “Supplement” is not helping the reader (title, section headers, and subsection headers). All tables in the other supplementary material lack numbering and titles. For ease of reference, please add numbering the supplementary materials (e.g. SI 1, SI 2).\n\nThe reference style is inconsistent in multiple ways, both in-text references and in the reference section. Examples can be found right next to each other, see for instance the top of p.3: (Jan C Minx et al. 2018; J.C. Minx, Lamb et al. 2017). The authors may have intended to clean such formalities up after receiving a journal decision, but this is inconsiderate to the reviewer.\n\nThe text is lacking clarity, which makes it hard to follow the intention of the authors in multiple places. The text needs a major edit of its writing. Some mistakes are basic, leaving the impression that the final text was not read from A-Z before submission. Some examples:\n• Repeated sentences, one of which is used three times (including the references) in addition to the mention abstract. E.g. “CDR is a critical component of any strategy.”, “the CDR literature is 3-4 times larger than previously estimated (Burns 2021; J.C. Minx, Lamb, et al. 2017).”\n• Smith, S. M. et al. 2023 is referenced six times in the first four paragraphs. Another article (of the last author) is mentioned 8 times. Repeating a reference can be warranted, but not to the degree shown here.\n• Figures are placed before they are introduced in the text.\n• Unclear, unnecessary, mistaken and repetitive use of words. Some of these can be guessed by a specialist, but this should not be necessary. [Note that my formatting seem to have disappeared in the manuscripttrackingsystem from the below list of examples. The formatting was meant to indicate a line or word copypasted from the submitted article and my comment in parenthesis]\n- e.g: important prerequisite (pleonasm); new scientific developments (tautology); actively remove CO2 (what is meant by active?) sustainably(?) scaling CDR; sustainably achieving gigatonne-scale; grows at ever(?) increasing rates; map out; methodologies vs. methods; “while research on other CDR methods” (are “other” those which are not land-based as mentioned earlier i.e. ocean-based?); Other biological CDR methods (the classification into biological CDR methods is not introduced previously); engineered CDR options (again, this is not an established category of CDR); growth rate of BECCS is…(BECCS doesn’t grow, the number of studies grow); Place-based research is important for … in-situ; efficacy (effectiveness?), efficiency (what type?); provide an assessment at scale (“at scale” does not make sense); Most studies focus on investigating the CDR method from a technical perspective where the method itself is investigated; topical areas (are these simply topics?); distinctly different (unclear meaning); while patenting activity has been active in BECCS (most active or only active?); the literature is dominated by biochar research today (today -among articles published in 2024, or in the accumulated literature as indicated elsewhere?); with a geographical centre in China (institutional affiliations with China or studies in Chinese contexts?); most recent 6th Assessment Report (there is only one AR6); On p. 19, first section: research designs vary substantially across CDR methods (…) across individual CDR options research designs vary substantially (These sentences are in the same section, unclear if they mean something different?); please be clear on the use of compare with and compare to; we find further a strong focus on research on the methods (I assume the CDR literature focuses on methods, not research on methods); relative to the research shares per technology (this wording occurs several places and is very hard to understand); one third of CDR research in the literature (redundant); more place-specific research is mentioned in the same sentence as more specific sub-national locations (unclear if these terms mean the same); compared to vs. compared with.\n• Section 2.4 mentions “Our framework”, but no framework is mentioned elsewhere.\n• In methods section: ClimateBERT is chosen here due to its better performance (see Supplementary Information). Please be more specific when there are multiple supplementary materials with multiple sections.\n• In methods: summarized in Figure 1 and SI-Figure 4. The authors must be referring to SI-Figure 5.\n• In the note to Figure 4: Please improve the descriptions of the panels, they are currently difficult to understand.\n• There is no Table 1, and Table 2 is never introduced in the text.\n• The panel descriptions of figure 3 doesn’t match the panel positions.\n• When referencing a panel in a composite figure, it helps the reader if the reference specifies the panel (e.g. see Figure 3A).\n• What is meant by support in “a stronger focus on scenarios is supported by the fact that we find a shift from experimental research to modelling work”.\n• Clarify when CDR in general refers to CDR in general and when it refers to a specific category.\n• The union of “Social science and economics” is the same as saying economics, since economics is a social science already. Do the authors mean social science or economics?\n• In section 3: The sentence “our systematic map is scalable to the entire research domain around CDR” is confusing, as it sounds like the map isn’t scaled yet.\n• The authors claim they perform the most comprehensive effort yet, but I assume something else than effort is meant.\n• In paragraph 3 it seems the authors aim to introduce a list of knowledge domains. If so, please introduce the list properly and clarify when one list element ends, and another begins.\nParagraphs 5-7 of the Introduction seem to partly overlap. It is also unclear why paragraphs 3 and 6 are separate (e.g. “a number of studies has attempted to synthesize these growing literatures [references]” and “the few available overviews of the field have become outdated [other references]”. The paragraph structure is unclear and confuses the reader. I suggest either a restructuring or making the structure of existing paragraphs clearer.\n\nThe abstract would benefit from more specificity. For instance, why not mention biochar instead of “very concentrated on specific CDR options”. The use of “policy and practice” as it stands in the abstract.\n\nIn Figure 2, it is unclear if the middle panel shows accumulated shares, or the shares for each year of publication. It is hard to see the information presented in the bottom panel. Could it be enlarged by moving the top figure (less interesting) to the appendix, alternatively splitting the bottom figure into two; one representing the two lines “all publications on…” and another with the CDR types?\n\nFigure 4, top panel. The axis label “publications mentioning technology” is confusing, especially given the text right before mentioned “technology research” as a category. If you mean “CDR method” (as used elsewhere), please be consistent.\n\nMiddle-panel: Many journals, such as Nature Communications, are interdisciplinary journals. Such a “research field” seem to be missing from the diagram.\n\nThe types of CDR are sometimes referred to as CDR methods – this causes a tiny moment of confusion, as it could refer to the methods used in the CDR literature. I suggest avoiding the use of CDR methods and stick to other terms e.g. options, types, technologies, techniques.\n\nOn the Supplementary material “Instructions for Coding”\nCDR-Syntra is described as a living database, but the use, purpose and accessibility of this database is not mentioned elsewhere. The “living”-feature is unclear to me, despite the elaboration (p1, list element 3); wouldn’t an automatically updated test dataset also require retraining the algorithm, as to ensure the training data is a valid sample of the complete dataset?\n\nThe readability of the many tables could be greatly improved with simple formatting, mainly to ensure that the tables are broken across pages as little as possible. Even if the authors planned to do such final formatting after the peer-review, it is considerate to the reviewers if such formatting is done before submission.\n\np. 19: The section “assessment” introduces a categorization of documents, which is not used in the manuscript. It would be an interesting addition, which could provide some of the lacking insight of what the large number of predicted studies focus on (and potentially into the constituents of the “technology” label).\n\nThe “technology” label is very general, if not outright unclear. This is also evident from the large share of documents with a “main focus” predicted as “technology” (89 %). This warrants a decomposition of the label, otherwise it contains little informational value. The description of the label is described in the “main focus”-table (in “Instructions for coding”) as “the most vague/catch-all label”, which is problematic since it suggest that coders assign difficult-to-classify documents to this category. This almost sounds as if the authors were already aware that this label could be improved from a re-naming or decomposition. Please do so.\n\nOn the “Supplement”-file\nA table of contents would be helpful.\n\nSI Figure 5: I suggest adding the number of manually screened and annotated documents to the figure, as to give the complete overview of the procedure.\n\nThe number of “documents after deduplication” differ in the main manuscript and in SI figure 5 (75,518 vs. 69,942).\n\nPlease give a brief introduction to the meaning of the indicators F1 and ROC and the the scoring scale (e.g. 0 to 1, where 1 means…).\n\nReferences (only for those not already in the article)\nAnderson, K., Buck, H. J., Fuhr, L., Geden, O., Peters, G. P., & Tamme, E. (2023). Controversies of carbon dioxide removal. Nat. Rev. Earth Environ., 4, 808–814. doi: 10.1038/s43017-023-00493-y\nBrian W Head, Reconsidering evidence-based policy: Key issues and challenges, Policy and Society, Volume 29, Issue 2, May 2010, Pages 77–94", "reviewer_doi": "10.1016/j.polsoc.2010.03.001\nCarton, W., Hougaard, I.-M., Markusson, N., &\nLund, J. F. (2023). Is carbon removal delaying\nemission reductions? WIREs Clim. Change,\n14(4), e826. doi: 10.1002/wcc.826\nFuhrman, J., Speizer, S., O’Rourke, P., Peters,\nG. P., McJeon, H., Monteith, S., ...Wang, F. M.\n(2024). Ambitious efforts on residual emissions\ncan reduce CO2 removal and lower peak\ntemperatures in a net-zero future. Environ. Res.\nLett., 19(6), 064012. doi:\n34\n10.1088/1748-9326/ad456d\nPrütz, R., Fuss, S., Lück, S., Stephan, L., &\nRogelj, J. (2024). A taxonomy to map evidence\non the co-benefits, challenges, and limits of\ncarbon dioxide removal. Commun. Earth\nEnviron., 5(197), 1–11. doi:\n10.1038/s43247-024-01365-z\nFankhauser, S., Smith, S. M., Allen, M.,\nAxelsson, K., Hale, T., Hepburn, C., ...Wetzer, T.\n(2022). The meaning of net zero and how to get\nit right. Nat. Clim. Change, 12, 15–21. doi:\n10.1038/s41558-021-01245-w\nHeck, V., Gerten, D., Lucht, W., & Popp, A.\n(2018). Biomass-based negative emissions\ndifficult to reconcile with planetary boundaries.\nNat. Clim. Change, 8, 151–155. doi:\n10.1038/s41558-017-0064-y\nWood Hansen, O., & van den Bergh, J. (2024).\nEnvironmental problem shifting from climate\nchange mitigation: A mapping review. PNAS\nNexus, 3(1), pgad448. doi:\n10.1093/pnasnexus/pgad448\nZickfeld, K., MacIsaac, A. J., Canadell, J. G.,\nFuss, S., Jackson, R. B., Jones, C. D., ...Zaehle,\nS. (2023). Net-zero approaches must consider\nEarth system impacts to achieve climate goals.\nNat. Clim. Change, 13, 1298–1305. doi:\n10.1038/s41558-023-01862-7\nReferences\nBerrang-Ford, Lea, A. R. Siders, Alexandra Lesnikowski, Alexandra Paige Fischer, Max W.\nCallaghan, Neal R. Haddaway, Katharine J. Mach, et al. 2021. “A Systematic Global\nStocktake of Evidence on Human Adaptation to Climate Change.” Nature Climate\nChange 11(11): 989–1000. doi:10.1038/s41558-021-01170-y.\nBornmann, Lutz, Robin Haunschild, and Rüdiger Mutz. 2021. “Growth Rates of Modern\nScience: A Latent Piecewise Growth Curve Approach to Model Publication Numbers\nfrom Established and New Literature Databases.” Humanities and Social Sciences\nCommunications 8(1): 1–15. doi:10.1057/s41599-021-00903-w.\nCallaghan, Max, Carl-Friedrich Schleussner, Shruti Nath, Quentin Lejeune, Thomas R.\nKnutson, Markus Reichstein, Gerrit Hansen, et al. 2021. “Machine-Learning-Based\nEvidence and Attribution Mapping of 100,000 Climate Impact Studies.” Nature\nClimate Change 11(11): 966–72. doi:10.1038/s41558-021-01168-6.\nSaran, Ashrita, and Howard White. 2018. “Evidence and Gap Maps: A Comparison of\nDifferent Approaches.” Campbell Systematic Reviews. doi:10.4073/cmdp.2018.2.\n35", "reviewer_1_attached": "Comment Response\nThis paper contributes a novel machine Thank you for taking the time to review our\nlearning-assisted systematic mapping methodology manuscript and for helping us improve it.\nto provide a comprehensive evidence map of the\nCDR literature. The manuscript is well-written and Regarding your main comments:\nmotivated. The written language is simple and easy\nto be read for people of a different background The First:\nnovelty of the study is also presented clearly and We have provided additional reasoning to support\nthe systematic mapping approach is our findings. The specifics can be found in the\ncomprehensively presented in the supporting responses to your individual comments below.\ndocuments. Overall, a very useful and very much Regarding recommendations for the research\nneeded tool. With some additional filters this tool community, any systematic map primarily\nhas the potential to provide a great service to the highlights evidence hubs suitable for thorough\nCDR community and climate change mitigation systematic reviews and identifies evidence gaps\nefforts in the wider context.\nwhere further research is essential to address\nHowever, I miss a reflection on the potential reason societal needs. Both aspects have been added to\nfor certain results, which would be helpful for the discussion section.\nfurther guidance. Also, the discussion could benefit\nfrom clear recommendations for the CDR Second:\ncommunity on where to focus on (and why?). You offered many excellent suggestions for\nenhancing the automated sorting through\nAlso, it would be great to see a progression of the machine learning, particularly by adding more\ntool to more specificity (which might have been layers to the sorting process or introducing new\noutside the scope of this work). For example:\ncategories. While we appreciate the validity and\n• For BECCS, further coding based on biomass potential value of each of these enhancements,\norigin, for DACCS, further coding based on type of they fall outside the scope of our current work\nsorbent/solvent used, etc.\nand are not feasible to pursue. The reasons\nThis would allow an overview of the CDR literature include:\nspace not only based on macro-CDR methods, but\ngoing more into the specific technology (e.g., 1. Significant Additional Workload:\nhigh-temperature vs. low-temperature DACCS).\nMachine learning relies on hand-labeled\n• For LCA studies categorize the function of the data. Implementing your request would\nsystem (heat generation, biofuels production, mean re-labeling around 6,000\napplication to soil, co2 sequestration), which would documents, which previously took eight\nhelp in refining the literature landscape overview. students about six months. This would\nconsiderably increase the workload,\nmaking it impractical within our current\nproject scope.\n\n2. Technical Challenges: Machine learning\nmodels require a substantial sample size\nto statistically evaluate patterns in text.\nHowever, some of the features you\nsuggested are likely to have rather small\nsample sizes, which will limit our ability to\nbuild reliable models. Additionally, our\nmodel relies on data from the titles and\nabstracts of papers, and certain\nfeatures—such as technical readiness\nlevel—are often not mentioned in these\n1\nsections. As a result, these features\ncannot be effectively captured by the\ncurrent machine learning approach.\nWe are currently working on a living product,\nmeaning that the data in this manuscript is\nconstantly updated and made available. Over\ntime we aim to constantly improve the data and\nmight add some of the features you suggested.\nWe are already working on additional classes of\nCDR options such as CCUS and Direct Ocean\nCapture. We will also invite other scholars to join\nthis effort so we get an open-source product with\ncommunity support.\nFor now, if somebody wants to add the features\nyou suggested we would argue that this can be\neffectively addressed through simpler methods\nthan applying machine learning, such as keyword\nsearches in titles and abstracts.\nWith regards to the content of the paper:\nWe added some reasoning behind some of the\nresults. Recommendations for the research\ncommunity coming from a systematic map mainly\nconsists of points of evidence hubs which can be\na source for a thorough systematic review, or\npointing to evidence gap where we as society\nneed more research to We added both parts to\nthe discussion.\n“In policy, CDR has gained increasing attention in We acknowledged your points here by adding the\nrecent years (Geden, Scott, and Palmer 2018; following sentence to the end of the cited\nSchenuit et al. 2021), but many countries still lack paragraph:\nconcrete policies to scale CDR (Smith, S. M. et al.\n2023) Chapter 5). This has led to a considerable “One of the challenges here is that there is a\ngap between countries’ (so far limited) plans to large spread of possible CDR levels that\ndevelop and deploy CDR versus CDR’s required countries might aim for, in part driven by model\nrole in mitigation scenarios that stabilise global assumptions of technological innovation and\ntemperatures at an increase well below 2°C. potential market adoption (Dekker et al. 2023)”\n(Bellamy 2018; Cox and Edwards 2019; Schenuit\net al. 2021; Smith, S. M. et al. 2023). (page 2,\nmain)\no I would specify that the quantitative information of\nCDR required in mitigation scenarios is obtained\nfrom model-based scenario analyses. Hence it is a\nnumber resulting from modeling assumption and\nmodel “fingerprints.” I would find it important to\nspecify that there is still a large spread of\nuncertainty associated with the scenario output: 1)\nStructural uncertainty (regarding technological\ninnovation and uptake, market behaviour, etc.\nAssumptions in the models), 2) Parametric\nuncertainties (involving differences in parameter\ncalibrations, regional and temporal scale) and 3)\nFundamental modelling choices (e.g., optimization\nversus simulation frameworks), model structure\nand foresight. (Dekker et al., 2023, Nature Energy)\nIn Figure 3, it would be interesting to see, for the In general, countries where lots of CDR research\nlocation-based research the correlations between is produced, produce also lots of location-based\nCDR methods and the specific geographic CDR research.\nlocations studied. Is there a difference between this\nmap and the results of Fig. 3a-3b?\nHowever, there is a difference when it comes to\nthe methods studied in the different world regions.\nThe reason for that is that we find a much higher\npercentage of nature-based solutions to be\nstudied with a regional focus compared to other\nmethods. This of course is then reflected in a\ndifferent focus of the world regions. However, this\nresult can be deducted from the results already\npresented in the paper.\nTo accommodate your remark, we produced the\nsame kind of figures which we used for 3a and\n3b, present them in the SI and refer to them as\nfollowing:\n“Further information on regional based research\nwith details to the specific regions can be found in\nthe SI.”\n“In long-term mitigation scenarios that achieve the It is the first case. The paragraph now reads\nParis long-term temperature goals (Byers et al.\n2022) mainly BECCS (99%), afforestation (67%) “In long-term mitigation scenarios that achieve\nand DACCS (29%) are the CDR options included. the Paris long-term temperature goals (Byers et\nThere is not a single scenario dealing with biochar al. 2022) mainly BECCS (99%), afforestation\nor soil carbon sequestration, although these (67%) and DACCS (29%) are the CDR options\nmethods can deliver co-benefits such as food included. There is not a single scenario dealing\nsecurity or N2O emission reduction (Lehmann et al. with biochar or soil carbon sequestration due to a\n2021).” lack of implementation of these methods despite\ntheir potential co-benefits such as food security or\nCan we contextualize this statement more? Do the N O emission reduction (Lehmann et al. 2021).”\n2\nmodels not include biochar and soil carbon\nsequestration in their representation (i.e., the\nreason why these methods do not appear in the\nscenarios), or are they represented, but the models\ndo not choose these methods in mitigation\npathways (if so, why)?\nCoding Framework: Long-term storage is defined We considered the 5 year horizon to be a good\nas at least 5 years. As 5-years is not a separator for dividing very short-term storage\nclimate-relevant time horizon, why wasn’t a longer from insecure storage in nature-based carbon\ntime horizon chosen in the definition of long-term pools such as forests. We added this explanation\nstorage when screening for CDR literature?\nin the coding guideline to make it clear for the\noutside.\nIn reality, papers rarely describe the time horizon\n3\nof storage in such a detailed way. It was rather\nused as a guideline to understand what we are\ninterested in.\nFigure 1 should be better explained. For example, We extended the figure caption. In Particular, we\nthe figure is not clearly explained in the text or named the counts of each produced dataset to\ncaption (I know the methods are at the end in NC avoid the confusion you experienced. It now\nbut this is not easy to follow and read to have this reads:\nfigure here). For example, it is unclear why and Figure 1: Overview of the data retrieval for this\nhow documents are coded by hand. Why is each study. Squares symbolise documents, a coloured\ndocument coded by two people, does this mean all\nsquare a document with labels, either assigned\n30,000 papers?\nby hand (solid colour) or automatically (faded\ncolour). Red documents are excluded, blue ones\nincluded. Step 1: 70,000 documents were\nretrieved from databases using search queries.\nStep 2: Of these about 6000 documents are\nsorted (=coded) by hand into being on CDR\n(relevant, blue squares) or being not on CDR\n(irrelevant, red squares). Documents on CDR\nwere additionally described with CDR method\nand other categories. Step 3 and 4: The\nrelevance labels and additional categories were\nused to train machine learning classifiers. Step 5:\nThe trained classifiers were used to extend all\nlabels to the unseen ~64,000 documents.\nDetailed information on methods can be found in\nthe method section and the Supplemental\nInformation.\nThe authors report the results consistently, It is difficult to pin these trends on any of the\nhowever, I would be interested for the reason of given factors. More detailed explanatory analysis\nreported results, e.g. why some CDR methods are - itself a major undertaking - is also out of scope\nmore popular in certain regions. I would suggest to for this article. We therefore highlight a series of\nelaborate on this in either the results or the drivers in the text that are likely to influence\ndiscussion. For example;\npublication rates, while acknowledging the\n‘China focuses more on biochar research, Europe importance of context:\non BECCS, and North America,\nparticularly the US, on DACCS relative to the “There could be a number of drivers that explain\nresearch shares per technology in all the large uptake of biochar research in China,\ncountries. Research on ocean-based CDR including institutional developments (e.g.\nmethods is more predominant in Oceania and increased core funding at agricultural universities,\nNorth America’ publishing incentives, or research grants),\nCan the authors give reasons why (in China)? For strengthening scientific networks (e.g. new\nexample, certain regimes, policies implemented, societies, journals, project collaborations and\nsocial pressure/acceptance? This would be useful to exchanges), or a concerted push from the policy\nbetter understand how certain aspects can impact sphere (e.g. strategic research funding, support\nresearch efforts.\nfor public-private enterprises). Of course, applied\nresearch cannot be abstracted from its\nsurrounding geographic and economic contexts.\nIt is therefore not unexpected to find CDR\nresearch niches in different contexts (e.g. biochar\n4\nin agriculturally productive regions, ocean based\nCDR in coastal regions, DACCS and BECCS in\nindustrialised regions).”\nI also do think that the results can be better We enhanced the key message in section 2.2, i.e.\nstructured in terms of paragraphs. Please think that the attention to different CDR methods is\nabout the key messages you want to provide. For very unevenly distributed. We further deleted\nexample, in section 2.2, the authors are going a bit several numbers from this paragraph and added\nback and forth between the number of studies of a reference to the table where all the numbers\nCDR options while I don’t think that for each are already noted.\nmethod one has to provide the results using its\nrelative share of overall number of studies.\nIf possible, please provide the search terms in the The full search queries are already provided in an\nmethods or SI.\nadditional file. We added a reference to them in\nthe method section.\nMaybe I missed it, but why is storing CO2 in We developed a query related to CCUS, under\nwooden buildings or concrete not considered as which the storage of CO₂ in wooden buildings is\nCDR option?\nincluded. We also coded around 300 documents\nfrom this query. However, due to the complexity\ninvolved in storing CO₂ in products (beyond just\nbuildings), we decided not to include this CDR\nmethod in the paper. It will instead be addressed\nin a separate piece.\nOn the one hand, I liked the benchmarking of the We agree that the distribution of IPCC citations\nresearch landscape with regard to the IPCC report. does not need to represent the underlying\nOn the other hand, I am not sure how useful it is, literature. Still, we think that our comparison is\ni.e. I am not sure whether the IPCC should reflect interesting to highlight if a particular trend, such\nthe overall underlying scientific literature or instead as the surge in biochar literature, goes unnoticed\nshould just choose the most relevant studies.\nby the IPCC. We added the following sentences\nto the paper to acknowledge this:\n“Although it is clear that the IPCC cannot assess\nall of the large and growing body of available\nresearch (J.C. Minx, Callaghan, et al. 2017), it is\nessential to understand which main topics are\nemphasised or overlooked. We acknowledge that\ndifferences between the two literature bodies can\narise from various factors. While we do not\npropose that the main topic distributions should\nnecessarily align, our objective is to emphasise\nthese differences and investigate their\nimplications. This consideration reflects Hume's\n\"is-ought\" problem, which posits that observations\nabout what is (the findings in the main literature\nbody) do not inherently dictate what ought to be\n(the findings in the IPCC topic distributions).”\nThe discussion/conclusions seem more like a We re-wrote the discussion section substantially\nrepetition of the results. They should be partly and specifically added a section on evidence\nrevised by provided a set of recommendations for hubs and evidence gaps to guide further research\n5\nthe CDR community.\nefforts.\nIt would be great if you mention somewhere in the The main advantages of using machine learning\npaper, what makes this tool so useful and be are that it allows us to analyze a much larger\nspecific about it. Highlight the added value of using number of papers, making the map more\nmachine learning but also the novelty aspect of comprehensive, and enables us to update the\nyour work.\nmap with new literature as it becomes available.\nThis point was previously mentioned in the first\nparagraph of the discussion, but we have now\nrewritten it for clarity. It now reads:\n“In this article we provide a comprehensive\nevidence map of the CDR literature. Our novel\nmachine learning assisted approach follows a\nsystematic mapping methodology (James,\nRandall, and Haddaway 2016; Saran and White\n2018), and automates key labour-intensive parts\nof the process (Haddaway et al. 2020). This\nallows our systematic map to be scalable to the\nentire research domain around CDR rather than\nbeing limited to a single niche area of literature\ndue to resource limitations (Nakagawa et al.\n2018). As a result, we were able to quantify the\nCDR research landscape in an unprecedented\nway. Moreover, the automated classification can\nalso be applied to newly published CDR\nresearch, representing a critical step forward in\naccelerating learning on CDR and providing\nhigh-quality evidence syntheses on the topic. This\nis particularly important, as we continue to be\nfaced with a rapidly growing evidence base.”\nThank you for this excellent suggestion. However,\nAbout the tool: If possible, it would be great to have we believe the machine learning classification\nsome additional filters in the tool itself;\nalready offers substantial functionality by enabling\n1. Filtering the papers by research gap would make filtering across all specified categories—such as\na major difference and position this tool above any CDR method, research methodology, and main\nexisting online tool used for literature mapping as context of the paper. Additionally, as proposed,\nof today. It would probably mean a lot of work but papers can be further enriched with custom\nthink about the functionality of your tool. This would keyword searches.\nin turn increase its usability and expose your work\nwidely in the research community and beyond. In To enable filtering papers by research gap would\naddition to this, it would also be very interesting to require first identifying what ought to be\nhave a discussion in the paper around what are the researched – a task that cannot be undertaken by\nmajor research gaps being addressed as of today.\nusing our approach that focuses on what is\nalready in the literature. Instead it would need the\ninput of the entire research community on where\nknowledge gaps are.\nOur approach empowers researchers to tailor\ntheir searches according to specific research\n6\ngaps, which are often highly dependent on the\ndisciplinary perspective and individual\nresearcher’s interests. In our tool, researchers\ncan easily locate the relevant papers\ncorresponding to their particular area of focus,\nwithout aiming to provide a comprehensive ideal\nstate of knowledge for the field.\n2. In the LCA studies, one of the most important As mentioned earlier, this is unfortunately out of\nelements when putting together the study is the scope. The same result could be more easily\nfunctional unit of the assessment which connects achieved through a simpler keyword search\nto the function of the system being analysed. For method.\nexample, if I am looking at an LCA study of\nbiochar, the system at hand could focus on the We added this thought to the discussion where\nenvironmental impacts of biochar production. Other we expanded the second paragraph\nfunctions biochar is used for concerns: a) energy “At the heart of our map of CDR research is a\nproduction, b) biofuel production c) biochar classification system trained with about 5,300\napplication to soil for carbon removal d) biochar manually labelled documents that is able to\nused for soil purification etc. My point is, it is of predict not only the relevance of a scientific\ncrucial importance that you add another layer of publication for the evidence map, but also the\nfilter where the LCA studies are filtered by the CDR method, the broad area of research and the\nfunction of the system. For example, as of now the research methodology applied.”\ntool results in roughly 200 LCA studies on biochar\nbut If I want to look only the studies on biochar by these sentences\napplication to soil, I will not get more than 6 to 10\nLCAs. This would be a major innovative element of “This literature base serves as a foundation for\nyour tool.\nfurther analysis and can be easily expanded with\nadditional features that provide more detailed\ndescriptions of the scientific literature. In this\ncontext, a simple keyword search can enrich the\nliterature landscape across a diverse range of\ninterests.”\n3. In the same line as the LCA part, it is very We hope we addressed this thought with the\nimportant to provide a filter that specifies the IAMs above comment.\nscenarios used for the studies that work with them.\nAnd this is another point that can be discussed in\nyour paper.\n4. Add another filter concerning the Technological We liked the idea of reflecting the CDR\nreadiness level (TRL) of the CDRs. In a way this technologies in the light of their TRL and added\ncould as well explain why there is a certain focus some sentences in the discussion.\non specific CDRs compared to some others. In\nterms of functionality this would be very useful for\ndifferent stakeholders beyond research (investors,\ngovernments) who want to learn mainly about the\ntechnologies that are ready for large scale\nimplementation.\nOther minor comments:\n7\n• Fig. 3 caption: “right” and “left” panel are inverted Done. Thank you!\nin the caption text\n• Data and Methods section, page 15, first We aligned the text and SI-Figure number. Thank\nparagraph: SI-Figure 5 instead of SI-Figure 4 you.\n• Supplementary figures and numbered incorrectly: Done. Thank you!\no E.g., SI Figure 1, related to Figure 3; instead of\nFigure 2\n• Can we not use machine learning in the title While we agree that \"Machine Learning\" is more\ninstead of using artificial intelligence, in the accurate, the title length requires us to use an\nmethods machine learning is used while in the title abbreviation. We believe \"AI\" is more widely\nAI (I am aware that machine learning is a subset of recognized than \"ML,\" so we have chosen to\nAI though).\nkeep it in the title.\n• I would refer to the online tool available on the We added a remark in the discussion and in the\nweb, somewhere in the paper. Is it open source data availability section that all data can be\nand maintained?\ndownloaded online.\n• I would suggest removing the last paragraph on We replaced the result-like paragraph with an\npage 4, the one that summarizes the findings in the outlook of the article:\nintroduction section. It is a great paragraph, but\nyou mention these findings in the abstract, “In this article, we first quantify the total volume of\ndiscussion and conclusion. I think it would help to CDR literature and examine its temporal trends,\nreduce any sense of repetitiveness.\nas well as dissecting the literature by individual\nCDR methods to highlight shifts in research\nfocus. Next, we investigate the origins of these\nstudies, exploring regional profiles and analysing\nresearch that specifies geographic locations to\nidentify patterns in CDR research distribution. We\nthen assess the focus of the studies, including the\nscientific methods employed, to understand how\nresearch approaches have evolved. Additionally,\nwe evaluate the representation of CDR literature\nin the recent IPCC report, comparing it to the\noverall CDR literature to highlight any\ndiscrepancies. Finally, we compare the attention\ngiven to different CDR methods across various\ncontexts, including Integrated Assessment Model\n(IAM) scenarios, deployment strategies, and\ninvestment patterns. “\nReviewer 2\nComment Response\nThank you for taking your time and helping us to\nI co-reviewed this manuscript with one of the improve our manuscript.\nreviewers who provided the listed reports. This is\npart of the Nature Communications initiative to\nfacilitate training in peer review and to provide\n8\nappropriate recognition for Early Career\nResearchers who co-review manuscripts.\nReviewer 3\nComment Response\nI co-reviewed this manuscript with one of the Thank you for taking your time and helping us to\nreviewers who provided the listed reports. This is improve our manuscript.\npart of the Nature Communications initiative to\nfacilitate training in peer review and to provide\nappropriate recognition for Early Career\nResearchers who co-review manuscripts.\nReviewer 4\nFrom the E-mail\nComment Response\nCarbon dioxide removal (CDR) and negative Thank you so much for taking your time to review\nemissions technologies are needed in all the manuscript and helping us to improve it. We\nscenarios that achieve global warming targets set provide responses to the specific\nby the Paris Agreement, and therefore are a recommendations in our comments below.\ncrucial consideration for policy makers. This\narticle utilizes emerging applications of artificial\nintelligence models to generate an automated\nsystematic map of available academic evidence\nrelevant for CDR. This allows the authors to\ncharacterize the distribution and extent of\nacademic evidence across relevant metadata\nvariables such as publication year, first author\naffiliation, intervention type, methodology and\nscientific discipline. In my view, an important and\nkey contribution of this research is discrepancy\nthat was revealed between the distribution of\nacademic evidence, policy and deployments\nacross different CDR technologies. This finding is\nparticularly timely over the coming decade, as\nthere a strong motivation to scale up CDR\n(particularly in novel technologies). This\nsystematic map provides valuable insight into\nknowledge gaps that can inform future research\npriorities, as well as under-exploited knowledge\nclusters that can inform CDR investment and\ndeployment.\nwhich represents a valuable application of how\nmachine learning tools can be harnessed to\nconduct evidence synthesis with a large enough\nscope that is relevant for policy makers. I\nrecommend that this article be accepted for\npublication, with revision. I have made most of\nmy comments in the attached annotated pdf file,\nhowever include more general comments below.\nIn particular, I think the discussion should\nelaborate more on key findings – specifically how\npatterns of CDR research differ compared to\npolicy and practice, as well as providing more\ninformation and clarity in the methods with\nrespect to the named entity recognition. For all\nthe figures, please verify that the color palettes\nused are colorblind friendly where possible.\nIntroduction\n- Many valuable points are presented in the We edited the parts of the introduction, which\nintroduction, however I think a few contours can introduce these ideas, for more clarity.\nbe better defined that would help to position this\nwork, its motivation and contribution – specifically For example, the point on the challenges of fast\nwith how increasing scientific literature interacts growing literatures now reads:\nwith trade offs between scope and available time “In the age of big literature (M. W. Callaghan, Minx,\nresources when conducting systematic evidence and Forster 2020; J.C. Minx, Lamb, et al. 2017;\nsyntheses, and how automated approaches can Nunez-Mir et al. 2016) - —where the scientific\nbe applied in this context.\nliterature grows at ever increasing\nrates—balancing a research question’s scope and\nthe resource demands for reviews, like reviewer\ntime, is increasingly challenging (Collaboration for\nEnvironmental Evidence. 2018).. As a result,\nsystematic mapping methodologies (systematic\nmaps, evidence gap maps, etc.) have been\ndeveloped by the evidence synthesis community\n….”\nWith regards to automation, we edited the\ncorresponding sentences to now read:\n“Traditionally, systematic maps have been\ncompiled manually and therefore are often limited\nin scope. Here, we use an approach that deploys\nmachine learning methods to automate labour\nintensive tasks to provide an assessment at scale.\n”\nWe hope that this improves readability.\n10\n- Many different types of interventions are Good idea! We followed your suggestion and hope\ndiscussed, and the inclusion of a typology figure you like the new figure 2 as much as we do.\nor table that provides a short description of the\nmain classes could be helpful to clarify the scope\nof interventions assessed in this map (essentially\na summary of the table in the Coding Guideline –\nCategories – 0 Technology (CDR Technologies).\nThis would also clarify the boundaries between\nthe different types used in Figure 2 – e.g. blue\ncarbon management can be a form/sub-type of\nsoil carbon sequestration? I would suggest\nincluding a figure like this and moving table 2 to\nthe supplement\nResults\n- More precision/evidence could be added to We cited classification performance and\nseveral statements (e.g. briefly citing F1 confidence intervals in the paper where we found it\nstatistics, confidence intervals, etc where helpful and not too disturbing for the reading\nrelevant) experience. Further, we extended the SI to include\nall confidence intervals.\n- Section 2.1 & Figure 2: It seems for all the Yes - thanks! We added\nmethods, that annual growth rate starts high and\nthen declines, perhaps because it is easier to “Growth in newly emerging research areas tends\ngrow by a larger percentage when absolute to be particularly high", "reviewer_done\nscales”": "replace with an 'em' dash\n“pull back global mean temperatures in an It is actually meant the latter. We re-wrote the\novershoot scenario;” - I think what is meant here paragraph. It can be found in a very similar\nis in the (likely) scenario where emissions would version in the recent IPCC report:\nlead to an overshoot in the absence of CDR? But “CDR has three distinct roles in climate change\nthe way it could read is if an overshoot occurs, mitigation ((IPCC 2022) WG3 Chapter 12): first,\nthen CDR can be used. Also citation needed\nreduce net CO₂ and GHG emissions in the near\nterm; second, to offset residual emissions from\n“hard to mitigate” sectors like industry,\nlong-distance transport, and agriculture in the\nmedium term (Buck et al. 2023; Luderer et al.\n2018); and third, CDR to support sustained\nnet-negative emissions in the long term, helping to\nlower global temperatures in overshoot scenarios\nand stabilising warming at or below 1.5°C ((IPCC\n2022) WG3 Chapter 12). “\n“CDR specifically in the land sector contributes We deleted this part to make the introduction\nto lowering net emissions today.”: Could cite the more concise.\nmost recent State of Carbon Dioxide Removal\nreport https://www.stateofcdr.org/\n“A stream of literature going We added following sentence: “This area has\nback to the first IPCC assessment reports has since broadened to include analogous\nconsidered the potential contributions of nature-based approaches in other ecosystems,\nenhanced natural sinks through afforestation or such as coastal blue carbon (Nellemann et al.\nsoil carbon sequestration to achieve net 2009), alongside options aimed at enhancing\nemissions reductions”: I suggest providing a bit ecosystems' ability to absorb and store CO₂", "reviewer_chronological\nliterature”": "This statement that BECCES is appearance in the literature.\nmostly discussed in a modelling context\nindicates that BECCS is not at the stage of To make this clear we changed the sentence to:\ndevelopment where it is being deployed -- if the “Bioenergy with carbon capture and storage\nsubject of development stage/technologicall (BECCS) technologies have gained prominence in\nreadiness is raised here then I think it should be\nthe early 2010s as an explicit option for achieving\nbriefly assessed for the other types of\nnegative emissions in the integrated assessment\ninterventions mentioned.\nmodelling (IAM) literature [...]”\n“such as ocean alkalinity enhancement (OAE)”: I The emphasis in this paragraph is the\nwould still classify this as enhancing natural development in time of the different CDR\nsinks (see comment above). Can also reference: technologies. We therefore leave it as it is and\nOschlies, A., Stevenson, A., Bach, L. T., Fennel, added the reference you suggested.\nK., Rickaby, R. E. M., Satterfi eld, T., Webb, R.,\nand Gattuso, J.-P. (Eds.): Guide to Best\nPractices in Ocean Alkalinity Enhancement\nResearch (OAE Guide 23), Copernicus\nPublications, State Planet, 2-oae2023,\nhttps://doi.org/10.5194/sp-2-oae2023, 2023.\n“to synthesise these growing literatures”: To We added “but were either limited in scope or\nbetter define the contours of what has already provide only broad overviews", "reviewer_2023": "Can also add the 2nd Done.\nedition released in June 2024\n“2016) - where”: em dash Done.\n“it is no longer possible for scholars to keep an The sentence reads now:\noverview of new scientific developments,\neven in specialised fields.”: This is a pretty “In the age of big literature (M. W. Callaghan", "reviewer_this\nmethodologies”": "rather than claiming causality", "reviewer_section": "nperhaps it is more accurate to say that\nsystematic maps can be an alternative to\n“To address this issue", "reviewer_automation”": "Connecting to my context.\nprevious comment, I think they are also more\nammenable to automation because a systematic\nmap does not require a validity assessment -- a\nstage in systematic reviews which requires a\nhuman to assess the bias of a research article\n“Systematic maps and evidence gap maps refer We deleted this text part as it is now mentioned\nto a group of evidence already earlier in the discussion.\n\nsynthesis methodologies that have been\ndeveloped to comprehensively map out the\navailable evidence on a particular research\ntopic”: See my previous comments on systematic\nmaps -- I think defining what a systematic map is\nshould be positioned when they are first\nmentioned.\n“Our study finds”: This paragraph may be better We replaced the result-like paragraph with an\npositioned in the Results section outlook of the article:\n“In this article, we first quantify the total volume of\nCDR literature and examine its temporal trends,\nas well as dissecting the literature by individual\nCDR methods to highlight shifts in research focus.\nNext, we investigate the origins of these studies,\nexploring regional profiles and analysing research\nthat specifies geographic locations to identify\npatterns in CDR research distribution. We then\nassess the focus of the studies, including the\nscientific methods employed, to understand how\nresearch approaches have evolved. Additionally,\nwe evaluate the representation of CDR literature\nin the recent IPCC report, comparing it to the\noverall CDR literature to highlight any\ndiscrepancies. Finally, we compare the attention\ngiven to different CDR methods across various\ncontexts, including Integrated Assessment Model\n14\n(IAM) scenarios, deployment strategies, and\ninvestment patterns. “\n“with high precision and recall", "reviewer_28": "nd placed greater emphasis\nalready contributed to this debate. To be more on the importance of emissions reductions.\nspecific: The abstract and introduction Additionally, we have reformulated the paragraph\nemphasizes CDR as a critical strategy to limit to closely align with the roles of CDR as outlined in\nglobal warming below 2°C. First, if any strategy is the recent IPCC report.\ncritical, it is emission reductions, whereas CDR is\na supplementary strategy (Zickfeld et al 2023). It reads now in the abstract:\nCDR is meaningless if not combined or preceded “Carbon dioxide removal (CDR) will play an\nby emission reductions (Fankhauser et al 2022). important role in any strategy to limit global\nThe authors mention three roles of CDR. The first\nwarming to well below 2°C.”\nrole warrants further qualification, since even for\nresidual emissions (hard-to-abate sectors),\nThe text in the introduction reads:\nemission reductions are a preferable strategy to\n“To comply with the Paris agreement and to limit\nCDR in terms of several indicators (Fuhrman et\nal 2024). The second role hinges on a misguided global warming well below 2°C rapid and deep\nfixation on the 2°C goal, rather than the objective GHG emissions reductions need to be\nof limiting global warming as much as possible. complemented with Carbon Dioxide Removal\nIn other words, if emission reductions do not\n(CDR) to actively remove CO from the\noccur rapidly, reductions (and not CDR) will only 2\natmosphere—potentially at very large scales by\nbecome more urgent to minimize further global\nmid-century and beyond (IPCC 2022; Smith, S. M.\nwarming – which should be the target of climate\nchange mitigation, rather than expecting CDR et al. 2023).\ncan be scaled to match current (i.e. unmitigated)\nemission levels. This fixation also ignores the CDR has three distinct roles in climate change\nexpected (and those unknown) detrimental\nmitigation ((IPCC 2022) WG3 Chapter 12): first,\neffects of temperature overshoot, even if\nreduce net CO₂ and GHG emissions in the near\ntemporary. The third role of CDR is indeed\nterm; second, to offset residual emissions from\nimportant, as some CDR options (land-based are\n“hard to mitigate” sectors like industry,\nmentioned) can lower net emissions and already\ndoes so (as mentioned by some of the long-distance transport, and agriculture in the\nco-authors in other publications). But any medium term (Buck et al. 2023; Luderer et al.\nland-based CDR is limited by exactly that – land. 2018); and third, CDR to support sustained\nIn addition, climate change is not the only\nnet-negative emissions in the long term, helping to\necological crisis, which means there are other\nlower global temperatures in overshoot scenarios\nchallenges to large-scale deployment of CDR\nand stabilising warming at or below 1.5°C ((IPCC\nbeside the limited potential for removals. Many\n2022) WG3 Chapter 12). Of course, CDR cannot\nCDR options are found to have negative\nenvironmental effects (see e.g. Heck et al, 2018; compensate for stringent emission reductions,\nPrütz et al, 2024; Wood-Hansen & van den which need to be prioritised even in hard to\nBergh 2024).\nmitigate sectors (Fuhrman et al. 2024). There are\nalso deep uncertainties with respect to how fast\nCDR can be sustainably scaled-up, and whether\nthe reversal of temperature overshoot can be\nsafely achieved (Schleussner et al. 2024). This\nunderlines the need to reduce emissions as fast as\npossible, while providing sufficient policy support\nthat CDR can actually deliver gigatons of removals\nin the second half of the 21st century (Nemet et al.\n2018; Smith, S. M. et al. 2023).”\nSpecific minor points\nhow to build CDR economies where the CDR reviewers 1 and 4.\n(…) provides economic revenues beside\nmitigating global heating. This is a very specific\nexample; an example of such literature would be\ninteresting. It is however unmotivated why CDR\nshould provide economic revenue beside\nmitigation – this seems a problematic\nrequirement.\nWhat is meant with a diverse range of We added some text to make the point clear:\ninterdisciplinary fields and why does it add to the “The diverse range of CDR options and\ncomplexity of reviewing literature? I would think interdisciplinary fields involved in CDR research\ninterdisciplinarity would enable reviews, in\nalso adds to the complexity of this task, as\ncontrast to unconnected fields.\nresearchers from different disciplines, each with\ntheir own specialised languages and\nmethodologies, may be working on the same\nissues without fully knowing or engaging with each\nother due to misaligned terminology.”\nIt is mentioned that “there is little CDR research This paragraph is deleted on request by reviewers\non the societal implications [of CDR] such as 1 and 4 to facilitate the reading.\npublic acceptance, governance”. The choice of\nwords here is imprecise and unfounded. The\nmention of societal implications is unclear –\nnowhere else are these mentioned. The reader is\nleft to speculate if societal implications are\nunderstood as a subset of studies predicted to\nfall in the “context”-classification as “Policy /\nGovernment” or “Public perception”, but none of\nthese equal “societal implications”. Note also that\npublic acceptance specifically refers to public\nperception of policy after implementation (as\nopposed to acceptability which is ex ante policy\nadoption).\nThe use of the Gini-coefficient to explain the We deleted that part.\ndistribution of the literature is unnecessary, as\nthere is no reason to expect an equal distribution.\nSection 2.4 refers to Keller et al. 2018 and Indeed, these references serve as examples and\nWerner et al. 2022, but it is unclear what the role we therefore added “see for example”.\nof these references is. Are they examples of\nstudies under the label “policy and governance”, We deleted “important” in this sentence.\nor are they meant to support a claim? If the\nformer, please help the reader, for instance by\nwriting, “see e.g.”. The same sentence claims\nthat “policy and governance” and “earth system”\nare important categories but doesn’t explain why.\nThe authors find that the reviewed BECCS We deleted this overinterpretation of our results.\nstudies mainly fall into the “modelling” category\n25\nand infer that this “points toward limited efforts to\nresearch the design of the technology itself”. This\ninference is invalid, why would an increase of\nmodelling studies using BECCS imply anything\nabout other research efforts into BECCS?\nIt is claimed that “There is not a single scenario This claim comes from a scenario database\ndealing with biochar or …”, but where does this compiled for the 1st edition of the State of CDR\nclaim from? Is it a result of the review?\nreport, which we cite in the corresponding figure\nand now also cite in the introduction of this\nsection.\np. 13-14 states that the ML-assisted approach As stated above, we follow the systematic\nenables the authors to be systematic in their mapping procedure and complement it with our ML\nprocedure, but as they also mention in the efforts. To make that clear we adjusted the\nintroduction there are also systematic reviews not sentence slightly:\nrelying on ML.\n“The reason for this is that our machine learning\nassisted approach enables us to be systematic in\nour procedure and at the same time achieve both\nhigh levels of precision and recall.“\nAs explained above, previous approaches not\nusing ML were simply less inclusive in their design\nof the search query, which is one of the main\nreasons why they find considerably less literature\non CDR.\nIt is stated that DACCS is a place-specific DACCS is indeed a place-specific CDR type, as it\nCDR-type, but as I understand it, DACCS might not only requires a substantial supply of renewable\nbe one of the least context-dependent types of energy, as you noted, but also nearby access to\nCDR, except for the importance of local access storage facilities to avoid long-distance\nto renewable energy? In the same paragraph is transportation of captured CO2. For instance, the\nthe trivial observation that there are fewer studies Roads2Removal project has conducted an\non novel technologies. analysis of local requirements for various CDR\nmethods in the USA, highlighting the spatial and\nlogistical considerations for DACCS\nimplementation (see\nhttps://roads2removal.org/#direct-air-capture )\nThe authors call for more humanities and social We agree with Head 2010 that decision-making is\nscience research to support evidence-based complex, contested, and shaped by value\ndecision making. While I have sympathy for the preferences and practical judgements as much as\npoint, the argument needs sharpening. First, the scientific evidence. However, this does not mean\nauthors should be aware of the critique of the that advocates for evidence-based policymaking\nnotion of evidence-based policymaking (see e.g. should give up their voice in the competitive\nHead 2010). Second, the humanities do not struggle for influencing decision-making. Indeed,\ncontribute to evidence-based policymaking, nor is to do so would leave the field wide open for\nit the ambition. interest groups that have a financial stake at play.\nFurthermore, social science and the humanities\nplay a vital role by addressing essential research\n26\nareas such as public perceptions, policy\nframeworks, governance, stakeholder engagement\nand equity concerns. By examining these aspects,\nthey contribute a comprehensive reflection on\npolicy making, encompassing both the policies\nthemselves and the broader social context in\nwhich they operate. We edited the respective\nsentences to now read:\n“Additionally, our results point to a need for more\nresearch on CDR in the social sciences and\nhumanities, for example to support\nevidence-based decision-making on questions of\ngovernance and equity. This type of research will\nbe increasingly important as focus shifts towards\nthe implementation of CDR at scale and policy\ndesign to support this, as is implied by the\nambition of net-zero targets in the context of\nrelatively slow action of mitigation policies.”\nThe authors suggest using their literature map to We have adjusted the language and framing of the\nidentify what has been “under-cited” by the IPCC. results that compare our literature map to the\nThis is an interesting idea and sounds potentially IPCC with regards to your previous comments. We\nuseful. It could be an interesting addition to the refer to these for more detail (e.g. p. 24-25 of this\npaper. If not, please elaborate how this idea document).\ncould be carried out. Beyond this, additional analysis focusing primarily\non citation patterns in the IPCC is out of scope for\nthis article.\nThe authors make an unbacked and imprecise We think this is a misunderstanding and have\nclaim about what drives political attention (i.e. renamed section 2.6 to “2.6 Different shares of\nthat relatively slow mitigation action will shift CDR options in other indicators of policy and\npolitical attention toward CDR). What political practice”, explicitly avoiding the term “indicators of\nattention - the media, the public, or that of attention in policy and practice”.\npolitical elites? It is entirely speculative that there\nshould be a causal relation between a lack of\nemission reductions and an increasing attention\ntoward CDR. If we are to speculate, the\nshort-term prospects of economic growth and job\ncreation through technology-exports associated\nwith investments in CDR combined with the hope\n(among vested interests) that CDR can enable\ncontinued fossil fuel extraction are more likely\ndrivers of elite political attention. How close we\nare to the 2°C goal is probably the least crucial\nfactor in where political attention goes. The\npolitical economy of climate change is tightly\nassociated with hegemonies and dominant\nprocesses of power i.e. capital accumulation.\nPlease provide colourblind-safe versions for the Thank you for this overlooked topic. We provided\nfigures in which colour is the only way to alternatives where needed in the supplement and\ndistinguish elements. These can be put in the reference them in the figure caption.\nsupplementary material. (See the Springer\nNature commitment to inclusion)\nThe main manuscript is lacking cut-off date for We added the exact cut-off dates in the text.\nthe search queries. The only place I can find it is\nin a figure in the supplementary material, but it is\nonly given by the month.\nIn section 3 “our classification system […] is not It can give a degree of relevance but we use it as\nonly able to predict the relevance”. Does it score a binary classifier. To make this clear we changed\nof the degree of relevance of each study, or is it a the text to:\nbinary sorting into relevant/irrelevant.\n“that is able to predict not only if a scientific\nSection 2.4 mentions the use of “journal publication is relevant for the evidence map”\ninformation” without further explanation.\nThe journal was used as a proxy for categorizing\npublications into research fields. We clarified this\nby editing the corresponding sentence:\n“Additionally, we used the journal, in which a\npublication appeared, to determine academic\ndisciplines in line with the relevant OECD\nCategory scheme [..]”\nThe difference between the annotation We edited the paragraphs to make this difference\nmentioned in paragraph 3 (screen and annotate) clear and referenced figure 1.\nand 4 (we further annotate) is unclear. Paragraph 3 now reads: “We manually screen and\nannotate a total of 5,339 documents – 100-600 per\nCDR method – if they should be included in the\nmap (distinction between blue and red squares in\nFigure 1)”\nParagraph 4 reads:\n“In accordance with the definitions in our protocol\n(Lück et al. 2022), we further annotated all\nrelevant scientific articles from our manually coded\ntraining and validation set with regard to the CDR\noptions covered [... explanation of all annotated\ncategories …] These additional categories are\nrepresented in Figure 1 by the different blue\nshades for each annotated relevant document.”\nThe authors claim to achieve high levels of recall Indeed, precision and recall are ratios that give the\nand precision. These terms are not my expertise, proportions of how many of the documents\nbut I understand recall to be a ratio, but it seems identified as relevant by the classifier were actually\nhere to be used in the sense of resulting in many relevant and how many of those that are actually\nsearch results. A footnote or explanation could relevant were identified. We explain precision and\nbe included in the supplementary material. The recall in the main text and explain all terms in more\nsame goes for precision; it is unclear how it is detail in the SI.\nmeasured. I understand these may be related to\nthe classifiers and their scores such as F1 and\nROC-AUC. Even though this is technical jargon,\nit can be explained in simple terms in the\n28\nsupplementary material.\nThe authors acknowledge that their classification With this sentence we want to point out that\nsystem is not perfect, which is a trivial point. It is manually compiled maps usually lack a validation\nthen argued that manually compiled evidence strategy. Because another reviewer requested a\nmaps are assumed to be perfect, which sounds change the sentence reads now:\nlike an unnecessary strawman.\n“However, our supervised machine learning\nprocedures involve in-depth validation and as such\nwe establish transparency about our uncertainty in\nquantifying the evidence base – something rarely\nprovided in manually compiled evidence maps,\nwhich are commonly viewed as gold standard”\nOn form and style\nThe lack of basic formatting in the supplementary In the SI we now\nmaterials should not need mentioning. Please - provided page numbers\nprovide page numbers in all documents. The lack - added a table of content with descriptive\nof formatting in the document headed names\n“Supplement” is not helping the reader (title, - we reference each figure and table in the\nsection headers, and subsection headers). All SI with SI figure 1, SI table 1, etc. in the\ntables in the other supplementary material lack figure/table caption\nnumbering and titles. For ease of reference, - The citation style in Nature is numerical.\nplease add numbering the supplementary We will change this once the paper is\nmaterials (e.g. SI 1, SI 2).\naccepted.\nThe reference style is inconsistent in multiple\nways, both in-text references and in the\nreference section. Examples can be found right\nnext to each other, see for instance the top of\np.3: (Jan C Minx et al. 2018; J.C. Minx, Lamb et\nal. 2017). The authors may have intended to\nclean such formalities up after receiving a journal\ndecision, but this is inconsiderate to the reviewer.\nThe text is lacking clarity, which makes it hard to We generally do not agree with this harsh\nfollow the intention of the authors in multiple judgment which is also not supported by the other\nplaces. The text needs a major edit of its writing. reviewers. However, we adjusted the text where\nSome mistakes are basic, leaving the impression we find this appropriate.\nthat the final text was not read from A-Z before\nsubmission. Some examples:\n• Repeated sentences, one of which is used The mentioned examples are no longer part of the\nthree times (including the references) in addition text.\nto the mention abstract. E.g. “CDR is a critical\ncomponent of any strategy.”, “the CDR literature\nis 3-4 times larger than previously estimated\n(Burns 2021; J.C. Minx, Lamb, et al. 2017).”\n• Smith, S. M. et al. 2023 is referenced six times Smith, S. M. et al. 2023 refers to a report, the\nin the first four paragraphs. Another article (of the State of CDR report with several chapters. We add\n29\nlast author) is mentioned 8 times. Repeating a chapter references to this reference to make it\nreference can be warranted, but not to the clear.\ndegree shown here.\nThe 8 times citation refer to J.C. Minx, Lamb, et al.\n2017 which is actually a previous similar study. It is\nthat often cited because we compare our - more\ncomprehensive - study with the old one. We\ndeleted one citation where this study is mentioned\nin a different context and appears alongside\nothers.\n• Figures are placed before they are introduced We changed the text and figure order for Figure 1.\nin the text.\nUnclear, unnecessary, mistaken and repetitive Thank you for your suggestions. We changed the\nuse of words. Some of these can be guessed by wording in most of the suggested cases and only\na specialist, but this should not be necessary. kept a few instances where we found them\n[Note that my formatting seem to have appropriate as they were.\ndisappeared in the manuscripttrackingsystem\nfrom the below list of examples. The formatting\nwas meant to indicate a line or word copypasted\nfrom the submitted article and my comment in\nparenthesis]\n- e.g: important prerequisite (pleonasm); new\nscientific developments (tautology); actively\nremove CO2 (what is meant by active?)\nsustainably(?) scaling CDR; sustainably\nachieving gigatonne-scale; grows at ever(?)\nincreasing rates; map out; methodologies vs.\nmethods; “while research on other CDR\nmethods” (are “other” those which are not\nland-based as mentioned earlier i.e.\nocean-based?); Other biological CDR methods\n(the classification into biological CDR methods is\nnot introduced previously); engineered CDR\noptions (again, this is not an established\ncategory of CDR); growth rate of BECCS\nis…(BECCS doesn’t grow, the number of studies\ngrow); Place-based research is important for …\nin-situ; efficacy (effectiveness?), efficiency (what\ntype?); provide an assessment at scale (“at\nscale” does not make sense); Most studies focus\non investigating the CDR method from a\ntechnical perspective where the method itself is\ninvestigated; topical areas (are these simply\ntopics?); distinctly different (unclear meaning);\nwhile patenting activity has been active in\nBECCS (most active or only active?); the\nliterature is dominated by biochar research today\n(today -among articles published in 2024, or in\nthe accumulated literature as indicated\nelsewhere?); with a geographical centre in China\n(institutional affiliations with China or studies in\nChinese contexts?); most recent 6th Assessment\nsection: research designs vary substantially\nacross CDR methods (…) across individual CDR\noptions research designs vary substantially\n(These sentences are in the same section,\nunclear if they mean something different?);\nplease be clear on the use of compare with and\ncompare to; we find further a strong focus on\nresearch on the methods (I assume the CDR\nliterature focuses on methods, not research on\nmethods); relative to the research shares per\ntechnology (this wording occurs several places\nand is very hard to understand); one third of CDR\nresearch in the literature (redundant); more\nplace-specific research is mentioned in the same\nsentence as more specific sub-national locations\n(unclear if these terms mean the same);\ncompared to vs. compared with.\nSection 2.4 mentions “Our framework”, but no We replaced “framework” with “ML approach”.\nframework is mentioned elsewhere.\nIn methods section: ClimateBERT is chosen here We replaced “Supplementary Information” with “SI\ndue to its better performance (see Table 2”.\nSupplementary Information). Please be more\nspecific when there are multiple supplementary\nmaterials with multiple sections.\nIn methods: summarized in Figure 1 and Done.\nSI-Figure 4. The authors must be referring to\nSI-Figure 5.\n• In the note to Figure 4: Please improve the It is not clear what the reviewer finds hard to\ndescriptions of the panels, they are currently understand and none of the other reviewer had a\ndifficult to understand. remark on the figure caption. We slightly edited the\ncaption to improve what we thought could be\nunclear.\nThere is no Table 1, and Table 2 is never We changed the numbering and added a\nintroduced in the text. reference to Table 1.\nThe panel descriptions of figure 3 doesn’t match Adjusted.\nthe panel positions.\nWhen referencing a panel in a composite figure, We always refer to the complete figure for each\nit helps the reader if the reference specifies the paragraph.\npanel (e.g. see Figure 3A).\nWhat is meant by support in “a stronger focus on Is replaced by “underlined”.\nscenarios is supported by the fact that we find a\nshift from experimental research to modelling\nwork”.\nClarify when CDR in general refers to CDR in Done.\ngeneral and when it refers to a specific category.\nthe same as saying economics, since economics want to put some emphasis on economics.\nis a social science already. Do the authors mean\nsocial science or economics?\nIn section 3: The sentence “our systematic map We edited the sentence as follows:\nis scalable to the entire research domain around “This allows our systematic map to be scalable to\nCDR” is confusing, as it sounds like the map isn’t the entire research domain around CDR rather\nscaled yet.\nthan being limited to a single niche area of\nliterature due to resource limitations”\nto highlight the advantages of our approach\ncompared to previous manual efforts.\nThe authors claim they perform the most We edited the sentence to now read: “While our\ncomprehensive effort yet, but I assume CDR map represents the most comprehensive\nsomething else than effort is meant. workeffort in this area to date, it does not offer a\ncomplete portrayal of CDR science.”\nIn paragraph 3 it seems the authors aim to It is unclear which paragraph 3 is meant.\nintroduce a list of knowledge domains. If so,\nplease introduce the list properly and clarify when\none list element ends, and another begins.\nThe abstract would benefit from more specificity. The parts in the abstract read now:\nFor instance, why not mention biochar instead of “Growth in CDR research was faster than for the\n“very concentrated on specific CDR options”. The field of climate change research as a whole, but\nuse of “policy and practice” as it stands in the\nvery concentrated in specific areas—such as\nabstract.\nbiochar, certain research methods like lab and field\nexperiments, and particular regions like China.\nPatterns of CDR research contrast significantly\nwith trends in patenting and CDR deployment,\npointing towards potential inefficiencies at the\nscience-policy interface.”\nIn Figure 2, it is unclear if the middle panel shows We added “per year” to make it clear. We find the\naccumulated shares, or the shares for each year above two panels more interesting and leave the\nof publication. It is hard to see the information figure as it is.\npresented in the bottom panel. Could it be\nenlarged by moving the top figure (less\ninteresting) to the appendix, alternatively splitting\nthe bottom figure into two; one representing the\ntwo lines “all publications on…” and another with\nthe CDR types?\nFigure 4, top panel. The axis label “publications We changed the axis label to “publications\nmentioning technology” is confusing, especially mentioning CDR option”.\ngiven the text right before mentioned “technology\nresearch” as a category. If you mean “CDR\nmethod” (as used elsewhere), please be\nconsistent.\nMiddle-panel: Many journals, such as Nature We follow the OECD Category scheme for\nSuch a “research field” seem to be missing from which does not include “interdisciplinary journals”.\nthe diagram. We added the hint + citation also to the figure\ncaption.\nThe types of CDR are sometimes referred to as We changed this terminology (previously “CDR\nCDR methods – this causes a tiny moment of methods”) to “CDR options” to avoid confusion\nconfusion, as it could refer to the methods used with the methodology categories.\nin the CDR literature. I suggest avoiding the use\nof CDR methods and stick to other terms e.g.\noptions, types, technologies, techniques.\nOn the Supplementary material “Instructions for The “living” part, meaning that the CDR map will\nCoding” continuously be updated, is part of a larger project\nCDR-Syntra is described as a living database, and not relevant for this paper. We deleted this\nbut the use, purpose and accessibility of this part from the coding guideline.\ndatabase is not mentioned elsewhere. The\n“living”-feature is unclear to me, despite the Further, the data is available on an online platform\nelaboration (p1, list element 3); wouldn’t an and this is now mentioned several times in the\nautomatically updated test dataset also require manuscript: In the discussion and the data\nretraining the algorithm, as to ensure the training availability section.\ndata is a valid sample of the complete dataset?\nThe readability of the many tables could be For the “Instructions for Coding” this is not feasible\ngreatly improved with simple formatting, mainly to due to the length of the tables. Since all tables,\nensure that the tables are broken across pages except for the first one, follow a consistent format -\nas little as possible. Even if the authors planned always consisting of the same three columns\nto do such final formatting after the peer-review, it (\"Label,\" \"Description/Definition,\" \"Rule\") - we\nis considerate to the reviewers if such formatting believe this should still allow readers to follow the\nis done before submission.\ncontent without much difficulty.\nHowever, we ensured that all tables in the SI\nappear fully on individual sheets for better\nreadability.\np. 19: The section “assessment” introduces a We find that the data does not give more insight\ncategorization of documents, which is not used in and excluded it from this study.\nthe manuscript. It would be an interesting\naddition, which could provide some of the lacking\ninsight of what the large number of predicted\nstudies focus on (and potentially into the\nconstituents of the “technology” label).\nThe “technology” label is very general, if not The “technology” label codes “Features,\noutright unclear. This is also evident from the processes, and the application of the technology” -\nlarge share of documents with a “main focus” a category which is very common. Examples for\npredicted as “technology” (89 %). This warrants a that are studies which investigate optimal filters for\ndecomposition of the label, otherwise it contains DACCS, field studies of biochar to be used in\nlittle informational value. The description of the agriculture or how mangrove afforestation affects\nlabel is described in the “main focus”-table (in “Instructions for coding”) as “the most fish habitats. The diversity already tells that there\n33\nvague/catch-all label”, which is problematic since are many studies of this kind.\nit suggest that coders assign difficult-to-classify Difficult-to-classify documents caused mostly a\ndocuments to this category. This almost sounds divergent labeling among the two coders which\nas if the authors were already aware that this were first discussed by them and if they did not\nlabel could be improved from a re-naming or come to a conclusion we would discuss it in a\ndecomposition. Please do so.\nlarger group and - if necessary - added more rules\nto the coding guideline.\nA decomposition of the label technology is out of\nscope of this study because it would require a\nre-labelling of most of the classified studies, a\nprocess which would take an enormous amount of\ntime as outlined earlier.\nOn the “Supplement”-file\nA table of contents would be helpful. Done.\nSI Figure 5: I suggest adding the number of The figure focuses on the data itself. The\nmanually screened and annotated documents to procedure itself is sketched in Figure 1. We do not\nthe figure, as to give the complete overview of see the need to repeat ourselves.\nthe procedure.\nThe number of “documents after deduplication” Adjusted.\ndiffer in the main manuscript and in SI figure 5\n(75,518 vs. 69,942).\nPlease give a brief introduction to the meaning of We added an explanation in the SI.\nthe indicators F1 and ROC and the the scoring\nscale (e.g. 0 to 1, where 1 means…).\nReferences (only for those not already in the\narticle)\nAnderson, K., Buck, H. J., Fuhr, L., Geden, O.,\nPeters, G. P., & Tamme, E. (2023). Controversies\nof carbon dioxide removal. Nat. Rev. Earth\nEnviron., 4, 808–814. doi:\n10.1038/s43017-023-00493-y\nBrian W Head, Reconsidering evidence-based\npolicy: Key issues and challenges, Policy and\nSociety, Volume 29, Issue 2, May 2010, Pages\n77–94"}, "author_response": "Reviewer 1\nReviewer’s comment Response\nThank you for making the revisions based Thank you for your positive feedback and\non the comprehensive set of comments. valuable input, which have significantly\nAlthough some of the comments cannot be contributed to improve our paper.\nentirely satisfied for various reasons, I\nbelieve the paper has been substantially\nimproved. I would advise to accept the\ncurrent version of the manuscript.\nReviewer 2\nReviewer’s comment Response\nI co-reviewed this manuscript with one of Thank you for your time and effort in\nthe reviewers who provided the listed helping to improve our paper.\nreports. This is part of the Nature\nCommunications initiative to facilitate\ntraining in peer review and to provide\nappropriate recognition for Early Career\nResearchers who co-review manuscripts.\nReviewer 3\nReviewer’s comment Response\nI co-reviewed this manuscript with one of Thank you for your time and effort in\nthe reviewers who provided the listed helping to improve our paper.\nreports. This is part of the Nature\nCommunications initiative to facilitate\ntraining in peer review and to provide\nappropriate recognition for Early Career\nResearchers who co-review manuscripts.\nReviewer 4\nReviewer’s comment Response\nI thank the authors for their diligent\nresponses to my revisions and comments,\nand support the revised manuscript for Thank you so much for your valuable input.\npublication. It really helped in improving the paper.\nThe additional elaboration on the role in\nadvancing the frontier of AI-assisted\nsystematic maps, as well as our\nunderstanding of the field\nof CDR better highlight the novelty and\ncontribution of this work, and the addition of\nthe new Figure 2 emphasizes the large\nperimeter of this synthesis.\nI feel that the revised document presents a\nclear explanation of the results, and\nwell-supported interpretation and evaluation\nof their relevance. The authors made\nvaluable additions for interpreting the\naccuracy and precision of their findings by\nincluding the performance and confidence\nintervals of the classifiers, as well as\nincluded numerous statements that better\ndefine the limitations of interpreting certain\npatterns and trends (e.g. high growth rates\nat low publication numbers in Fig 3). The\nrevised section \"2.5 IPCC reports,\npolicy-making\nand practice have foci different from the\nscientific literature\" enriches the\ncomparison between the distribution of\nacademic literature\nand publications informing policy. With\nregards to methodology, my previous\nrequest to add clarity with regards to the\nprocess for named\nentity recognition has been addressed.\nReviewer 5\nReviewer’s comment Response\nThe authors have thoroughly revised the Thank you once again for taking the time to\nmanuscript, and I now recommend help improving our paper.\nacceptance given some minor revisions.\n### Feedback on the reply to reviewer We sincerely apologize if we gave the\nimpression that we do not value your\nI found the tone of the reply document less specific input - this was absolutely not our\nthan collegial and in places, replies intention. We deeply respect the scientific\nappeared directed more toward the editor peer review process and, from our own\nthan the experience, understand the significant effort\nreviewer. This impression stems from required to provide constructive and\nseveral observations: valuable feedback. We are truly grateful for\nthe time and effort you have dedicated to\n1. The authors repeatedly refer to other helping us improve.\nreviewers, which is irrelevant to my review\nprocess, as I had no access to their Thank you also for your detailed feedback\nfeedback. Replying to a comment is more on our comments. We appreciate your\nconvincing that referring to the judgment of dedication and will take your suggestions\nanother reviewer. into account in our future responses.\n2. Phrases like _\"this part has been deleted\non request by reviewers 1 and 4\"_ appear\ntwice in reply to my comments.\nAcknowledging reviewer 5’s feedback\nequally would have been appropriate.\n3. Statements such as _\"the decision\nregarding whether we should submit\nelsewhere ultimately lies with the editor\"_\nare unnecessary and\ndismissive. This is self-evident, but\nreviewers are expected to make\nrecommendations to the editor; I specifically\nused the term \"encourage.\"\nThe authors seem more annoyed than\nappreciative of the peer-review process.\nThis is disheartening given the voluntary\nnature of reviewing and the detailed\nfeedback provided in my first review, which\nincluded examples and actionable\ncorrections.\nThe authors note that they _\"generally do\nnot agree with this harsh judgment\"_\nregarding clarity issues but nonetheless\nimplemented most of my detailed\nsuggestions to improve the writing. This\ncontradiction suggests either a reluctance\nto accept critique or a lack of clarity in their\nrebuttal. Please either accept critique or\ndefend. This is more transparent.\nYour research is valuable, and ensuring that\nthe writing is as clear and precise as\npossible will increase its impact and reach. I\nsuggest that\nthe main authors consult the Nature guides\non writing ([link\n1](https://www.nature.com/articles/nphys72\n4\n<https://www.nature.com/articles/nphys724\n>), [link\n2](https://www.nature.com/articles/nmeth.45\n32\n<https://www.nature.com/articles/nmeth.453\n2>), [link\n3](https://www.nature.com/nature-portfolio/f\nor-authors/write\n<https://www.nature.com/nature-portfolio/for\n-authors/write>)), which\noffer helpful advice for improving clarity and\naccessibility in scientific writing. These\nguides include relevant tips such as\navoiding\n\"elegant\" variance and hyperbole. It was\nnice to see the authors removed most of the\nelegant variance from the initial submission\n(e.g.\nconsistently using \"CDR options\").\nLastly, on reference styles: The issue is not\nthat the manuscript was submitted in APA\nstyle, but that the referencing was done\ninconsistently. Defending this by noting that\nNature will standardize the references later\nis insufficient. As I noted in my initial review,\nsuch carelessness is inconsiderate to\nreviewers, who rely on clarity and\nconsistency to evaluate the manuscript\neffectively.\n###### Remaining comments\n1: In my first review I highlighted several We apologize for this mistake and do not\nmistakes that suggested carelessness and want to give the impression that we are\nrequested greater attention to internal careless. On the contrary, we try to address\nrevisions of the writing. That the main text all your comments.\nof the revised manuscript starts with a typo\nand a misplaced comma \"...reductions ,\nneed...\" is not reassuring. Such oversights\nare inconsiderate to reviewers, who\nvolunteer their time to improve the work.\nPlease be kind to your peers.\nThe authors report finding very little We believe there may be a\nresearch on BECCS, but this may result misunderstanding. We classify any\nfrom not including broader research on research as being on BECCS if it discusses\nbioenergy and CCS. While I have not CCS in conjunction with bioenergy as we\nverified this directly, I suggest that the have also fixed in the Coding Guideline. We\nauthors provide a brief reflection on this are confident that the finding of limited\npotential limitation in the discussion. research on BECCS accurately reflects the\nfact that comparatively little work has been\nconducted in this area.\nI didn't receive a reply to my request for a We do not report on ~70,000 articles\ncritical reflection on the stark difference in related to CDR but rather focus on ~29,000\nresults from the authors’ initial search query articles. This difference arises from a\n(~70,000 articles) compared to two earlier distinct search query and search strategy.\nreviews (2,900 and 6,200 articles in 2017 The mentioned previous approach did not\nand 2018, respectively). The 1,000% apply filtering after running the search\nincrease in five query. To ensure a high proportion of\nyears is striking and warrants exploration. A relevant documents (i.e., high precision)\ncomparison of search strategies or without additional filtering, they employed\nreapplying earlier queries to recent data highly restrictive search queries, which may\ncould offer have excluded some relevant documents\nalternative explanations for this difference.\n(resulting in low recall).\nIn this study, we adopted a different\napproach using machine learning to deploy\nan additional filtering step. Initially, we used\nbroader search queries to capture as many\nrelevant documents as possible (high\nrecall), even if this included many irrelevant\nones (low precision), resulting in the\n~70,000 documents you referenced. In the\nsecond step, machine learning was used to\nfilter out irrelevant documents, allowing us\nto achieve both high recall and high\nprecision.\nWe believe this process is already\nadequately explained in the first paragraph\nof the results section of the paper:\n“There is a much larger body of CDR\nresearch than previously suggested. Based\non our machine learning assisted approach\nthat enables us to identify CDR studies with\nhigh precision (0.88±0.0119, meaning the\nproportion of relevant studies among those\nidentified is high) and recall (0.93±0.005,\nindicating most relevant studies are\ncaptured) - see Methods, SI and Figure 1 -\nwe predict a total of 28,976±3800 scientific\nstudies in the Web of Science and Scopus\n(the two largest bibliographic core\ncollections). This is 3-4 times larger than\nwhat previous scientometric studies (J.C.\nMinx, Lamb, et al. 2017) or ongoing\ncommunity efforts to manually track CDR\nresearch (Burns 2021) have suggested\nwhen comparing the same time range. For\nthe former study, this discrepancy likely\narises from their reliance on non-machine\nlearning methods, which forced a\nhigh-precision, low-recall search approach.\nIn the case of the manual tracking efforts,\nthe rapid expansion of CDR literature has\nsimply made comprehensive tracking\nunfeasible.”\nWhile not a major issue, the authors We truly believe that tasks requiring little to\nprovided a lacking reply to my comment no intellectual or creative input should be\nstarting with \"In paragraph 8, it is argued\". automated whenever possible. The\nThey reply that 10.5 days per estimate of 10.5 working days per person is\nauthor/research assistant (=366 workdays just that—an estimate. In reality, no one can\nspread over 26 authors and 7 research sustain such work full-time. Personally, I\nassistants) for data collection would bean find it impossible to engage in this type of\nunwise use of their time. Many researchers, task for more than 1–2 hours a day.\nespecially those conducting fieldwork or\nexperiments, may find this perspective Comparing this kind of automation to\ndismissive. This is particular grating since fieldwork is not appropriate, as fieldwork\nyou have funding for 7 research assistants. inherently cannot be automated. We view\nLess arrogance is always appreciated. As a our stance not as arrogant but as a wise\nside note, AI investments reflect monetary allocation of intellectual resources.\nvalue to investors rather than social value,\ncontrary to the implication in the Moreover, automation offers the distinct\nmanuscript. advantage of being repeatable for newly\npublished work, making it highly scalable.\nWe have already addressed these points in\nour previous response and have adjusted\nthe text in the document accordingly after\nour first response.\nTo my comment starting with \"In paragraph Since we addressed the reviewers’\n3 it seems the authors aim to introduce a comments in order of their numbering, this\nlist of knowledge domains\", the authors section was deleted in a previous edit. We\nreply that it is unclear which paragraph is apologize for the inaccuracy in our earlier\nmeant. This is a lazy and disingenuous reply, which should have stated: “This part\nreply, since the third paragraph after the was deleted in a previous edit.”\n\"Introduction\" (in the original\nsubmission) header says \"CDR has been a\nkey part of climate change mitigation\ndiscussions in the scientific literature, but\nhas often been discussed in distinct\nknowledge domains\".\nThe abstract reads \"pointing towards We replaced that part with: “highlighting the\npotential inefficiencies at the science-policy differing development stages of CDR\ninterface\". First and minor, this is technologies.” to be consistent with the\nunnecessarily vague.\nrelevant paragraph on this in the discussion\nMore importantly, after comments by the section.\nreviewer(s), the authors admit throughout\nthe article that the number of studies should\nnot necessarily be mirrored in policy. It\nseems the authors have forgotten their own\nimprovement in this quoted sentence of the\nabstract.\nMinor:\n- \"stabilising warming at or below\" Why We agree. “At or below” emphasizes that it\nstabilize at 1.5°C if net-negative emissions is indeed possible to go below 1.5°C.\nare possible? This could be reconsidered,\neven if it was written as such in the most\nrecent IPCC.\n- \"deploy CDR versus CDR's required role\". We replaced “required” with “estimated”.\nThis sentence sounds like massive use\nCDR is required by the real-word, but it\nprimarily reflects the assumptions of model\nscenarios. Rephrasing could clarify this\ndistinction.\n- As noted earlier, a rate implies speed, and Thanks, we implemented this suggestion.\nan increasing rate implies acceleration.\n\"Ever increasing rates\" would represent the\nthird derivative (\"jerk\") in physics, which\nseems unlikely. \"Increasing rates\" would\nsuffice for clarity.\n- Most writing guides recommends avoiding We deleted “great” and “strong” in the given\nexaggerated phrases like \"great benefit to sentences.\nthe research community\" or \"a strong\nfocus,\" as they can weaken arguments\nrather than strengthen them.\nSection 2.3 finds that 30 % of all CDR We do not emphasize a conclusion that\nfirst-authors are from China, and 13 % is China is disproportionately\nfrom the US. The Chinese and US overrepresented. Actually, we would prefer\npopulations constitute 17 % and 4 % of the not to highlight this issue at all, as we don’t\nworld population, Without such context, know what a proportional representation of\nreaders may global research would look like, given that\nincorrectly conclude that China is countries have different capacities and\ndisproportionately overrepresented, research foci.\nwhen the opposite is true.\n\"our intuition suggests that this proportion\nshould be even greater\". This statement Thanks for this feedback. We have decided\nseems unfitting, particularly after to refer back to one of the IPCCs stated\nemphasizing that \"ought ≠ is.\" Additionally, goals, which is to comprehensively evaluate\nthe term \"this proportion\" is unclear and\nthe available scientific literature.\ncould benefit from more specificity.\nThus, we changed the previous version:\n“While the IPCC includes a relatively higher\nproportion of reviews (19% vs. 15%) and\nsystematic reviews (3% vs. 1%) compared\nto the overall CDR literature, our intuition\nsuggests that this proportion should be\neven greater to help capture and provide\naccess to the extensive uncited literature.”\nAs follows:\n“While the IPCC includes a relatively higher\nproportion of reviews (19% vs. 15%) and\nsystematic reviews (3% vs. 1%) compared\nto the overall CDR literature, we believe\nincorporating even more of these could\nfurther enhance its ability to fulfil a stated\ngoal of the IPCC - which is to\ncomprehensively evaluate the available\nevidence.”\n\"A stronger focus on scenarios is For clarity, we change it to: “The fact that\nsupported\". It is unclear whether this is a IPCC assessments have tended to focus on\nsuggestion or a description. Clarifying the scenarios is underlined by…”\nintended message would help readers.\n\"to be scaleable\". This phrasing is “This allows our systematic map to be\nconfusing if the benefit has already been scalable to the entire research domain\nrealized, as is the case, now that the study\naround CDR”\nis performed.\nis replaced by\n“This allows our systematic map to cover\nthe entire research domain around CDR”\n\"diverse range of CDR options and We replaced “interdisciplinary” with\ninterdisciplinary fields\". I apologise “multidisciplinary”.\nthat my initial comment was unclear. I\nmeant to note that interdisciplinarity is\ndifferent from multidisciplinarity. In the\nrevision, the authors make a valid\nargument, but it seems they may be\nreferring to multidisciplinarity (i.e.,\n\"researchers from different disciplines\")\nrather than interdisciplinarity. If this is not\nthe case, the phrasing is somewhat\nconfusing, and additional clarification would\nhelp ensure the intended meaning is clear\nto readers.\nTestament to the difficulty of clear writing, I We believe that the phrase “support\nwas also unclear in my comment on the role evidence-based decision-making” implies\nof social science in evidence-based policy. that decision-making is not necessarily\nWhen I said the humanities do not driven by the social sciences, but involves a\ncontribute to the evidence-base, it was not mutual exchange between science and\nto say that humanities are irrelevant or policy.\nwithout value, rather the opposite. The\nrevision says \"social sciences and We see no reason to distinguish the\nhumanities, for example to support humanities in this process, as even\nevidence-based decision-making\", but this interpretive evidence (for instance on the\nmisses my point. While the humanities offer different values implicit in policy pathways)\nvaluable insights, they do not contribute to can be valuable to clarify different positions.\nevidence-based policy because their Is it sufficient that we do not use the word\ncontributions are interpretive rather than “empirical” here?\nempirical. My intention was to highlight this\ndistinction, not to diminish the importance of\nthe humanities in broader decision-making\nprocesses."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Thank you for making the revisions based on the comprehensive set of comments. Although some of the comments cannot be entirely satisfied for various reasons, I believe the paper has been substantially improved. I would advise to accept the current version of the manuscript.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "I thank the authors for their diligent responses to my revisions and comments, and support the revised manuscript for publication.\n\nThe additional elaboration on the role in advancing the frontier of AI-assisted systematic maps, as well as our understanding of the field of CDR better highlight the novelty and contribution of this work, and the addition of the new Figure 2 emphasizes the large perimeter of this synthesis.\n\nI feel that the revised document presents a clear explanation of the results, and well-supported interpretation and evaluation of their relevance. The authors made valuable additions for interpreting the accuracy and precision of their findings by including the performance and confidence intervals of the classifiers, as well as included numerous statements that better define the limitations of interpreting certain patterns and trends (e.g. high growth rates at low publication numbers in Fig 3).\n\nThe revised section \"2.5 IPCC reports, policy-making and practice have foci different from the scientific literature\" enriches the comparison between the distribution of academic literature and publications informing policy. With regards to methodology, my previous request to add clarity with regards to the process for named entity recognition has been addressed.", "reviewer_5": "The authors have thoroughly revised the manuscript, and I now recommend acceptance given some minor revisions.\n\n### General Comments\nThe authors have addressed most of my initial comments. They have rewritten entire sections, improved the clarity of their writing, removed hyperbolic language, introduced Carbon Dioxide Removal (CDR) in a more balanced way, and provided stronger reasoning for their results. Collectively, these changes make the article more accessible and nuanced in its findings.\n\nSeveral new and interesting paragraphs have been added to the discussion. However, there remains some repetition from the results and introduction, offering an opportunity to shorten the text for improved readability.\n\n### Feedback on the reply to reviewer\nI found the tone of the reply document less than collegial and in places, replies appeared directed more toward the editor than the reviewer. This impression stems from several observations:\n\n1. The authors repeatedly refer to other reviewers, which is irrelevant to my review process, as I had no access to their feedback. Replying to a comment is more convincing that referring to the judgment of another reviewer.\n2. Phrases like _\"this part has been deleted on request by reviewers 1 and 4\"_ appear twice in reply to my comments. Acknowledging reviewer 5’s feedback equally would have been appropriate.\n3. Statements such as _\"the decision regarding whether we should submit elsewhere ultimately lies with the editor\"_ are unnecessary and dismissive. This is self-evident, but reviewers are expected to make recommendations to the editor; I specifically used the term \"encourage.\"\n\nThe authors seem more annoyed than appreciative of the peer-review process. This is disheartening given the voluntary nature of reviewing and the detailed feedback provided in my first review, which included examples and actionable corrections.\n\nThe authors note that they _\"generally do not agree with this harsh judgment\"_ regarding clarity issues but nonetheless implemented most of my detailed suggestions to improve the writing. This contradiction suggests either a reluctance to accept critique or a lack of clarity in their rebuttal. Please either accept critique or defend. This is more transparent.\n\nYour research is valuable, and ensuring that the writing is as clear and precise as possible will increase its impact and reach. I suggest that the main authors consult the Nature guides on writing ([link 1](https://www.nature.com/articles/nphys724), [link 2](https://www.nature.com/articles/nmeth.4532), [link 3](https://www.nature.com/nature-portfolio/for-authors/write)), which offer helpful advice for improving clarity and accessibility in scientific writing. These guides include relevant tips such as avoiding \"elegant\" variance and hyperbole. It was nice to see the authors removed most of the elegant variance from the initial submission (e.g. consistently using \"CDR options\").\n\nLastly, on reference styles: The issue is not that the manuscript was submitted in APA style, but that the referencing was done inconsistently. Defending this by noting that Nature will standardize the references later is insufficient. As I noted in my initial review, such carelessness is inconsiderate to reviewers, who rely on clarity and consistency to evaluate the manuscript effectively.\n\n###### Remaining comments\n1: In my first review I highlighted several mistakes that suggested carelessness and requested greater attention to internal revisions of the writing. That the main text of the revised manuscript starts with a typo and a misplaced comma \"...reductions , need...\" is not reassuring. Such oversights are inconsiderate to reviewers, who volunteer their time to improve the work.\nPlease be kind to your peers.\n\nThe authors report finding very little research on BECCS, but this may result from not including broader research on bioenergy and CCS. While I have not verified this directly, I suggest that the authors provide a brief reflection on this potential limitation in the discussion.\n\nI didn't receive a reply to my request for a critical reflection on the stark difference in results from the authors’ initial search query (~70,000 articles) compared to two earlier reviews (2,900 and 6,200 articles in 2017 and 2018, respectively). The 1,000% increase in five years is striking and warrants exploration. A comparison of search strategies or reapplying earlier queries to recent data could offer alternative explanations for this difference.\n\nWhile not a major issue, the authors provided a lacking reply to my comment starting with \"In paragraph 8, it is argued\". They reply that 10.5 days per author/research assistant (=366 workdays spread over 26 authors and 7 research assistants) for data collection would bean unwise use of their time. Many researchers, especially those conducting fieldwork or experiments, may find this perspective dismissive. This is particular grating since you have funding for 7 research assistants. Less arrogance is always appreciated. As a side note, AI investments reflect monetary value to investors rather than social value, contrary to the implication in the manuscript.\n\nTo my comment starting with \"In paragraph 3 it seems the authors aim to introduce a list of knowledge domains\", the authors reply that it is unclear which paragraph is meant. This is a lazy and disingenuous reply, since the third paragraph after the \"Introduction\" (in the original submission) header says \"CDR has been a key part of climate change mitigation discussions in the scientific literature, but has often been discussed in distinct knowledge domains\".\n\nThe abstract reads \"pointing towards potential inefficiencies at the science-policy interface\". First and minor, this is unnecessarily vague. More importantly, after comments by the reviewer(s), the authors admit throughout the article that the number of studies should not necessarily be mirrored in policy. It seems the authors have forgotten their own improvement in this quoted sentence of the abstract.\n\nMinor:\n- \"stabilising warming at or below\" Why stabilize at 1.5°C if net-negative emissions are possible? This could be reconsidered, even if it was written as such in the most recent IPCC.\n- \"deploy CDR versus CDR's required role\". This sentence sounds like massive use CDR is required by the real-word, but it primarily reflects the assumptions of model scenarios. Rephrasing could clarify this distinction.\n- As noted earlier, a rate implies speed, and an increasing rate implies acceleration. \"Ever increasing rates\" would represent the third derivative (\"jerk\") in physics, which seems unlikely. \"Increasing rates\" would suffice for clarity.\n- Most writing guides recommends avoiding exaggerated phrases like \"great benefit to the research community\" or \"a strong focus,\" as they can weaken arguments rather than strengthen them.\n\nSection 2.3 finds that 30 % of all CDR first-authors are from China, and 13 % is from the US. The Chinese and US populations constitute 17 % and 4 % of the world population, Without such context, readers may incorrectly conclude that China is disproportionately overrepresented, when the opposite is true.\n\n\"our intuition suggests that this proportion should be even greater\". This statement seems unfitting, particularly after emphasizing that \"ought ≠ is.\" Additionally, the term \"this proportion\" is unclear and could benefit from more specificity.\n\n\"A stronger focus on scenarios is supported\". It is unclear whether this is a suggestion or a description. Clarifying the intended message would help readers.\n\n\"to be scaleable\". This phrasing is confusing if the benefit has already been realized, as is the case, now that the study is performed.\n\n\"diverse range of CDR options and interdisciplinary fields\". I apologise that my initial comment was unclear. I meant to note that interdisciplinarity is different from multidisciplinarity. In the revision, the authors make a valid argument, but it seems they may be referring to multidisciplinarity (i.e., \"researchers from different disciplines\") rather than interdisciplinarity. If this is not the case, the phrasing is somewhat confusing, and additional clarification would help ensure the intended meaning is clear to readers.\n\nTestament to the difficulty of clear writing, I was also unclear in my comment on the role of social science in evidence-based policy. When I said the humanities do not contribute to the evidence-base, it was not to say that humanities are irrelevant or without value, rather the opposite. The revision says \"social sciences and humanities, for example to support evidence-based decision-making\", but this misses my point. While the humanities offer valuable insights, they do not contribute to evidence-based policy because their contributions are interpretive rather than empirical. My intention was to highlight this distinction, not to diminish the importance of the humanities in broader decision-making processes."}, "author_response": "Reviewer 1\nReviewer’s comment Response\nThank you for making the revisions based Thank you for your positive feedback and\non the comprehensive set of comments. valuable input, which have significantly\nAlthough some of the comments cannot be contributed to improve our paper.\nentirely satisfied for various reasons, I\nbelieve the paper has been substantially\nimproved. I would advise to accept the\ncurrent version of the manuscript.\nReviewer 2\nReviewer’s comment Response\nI co-reviewed this manuscript with one of Thank you for your time and effort in\nthe reviewers who provided the listed helping to improve our paper.\nreports. This is part of the Nature\nCommunications initiative to facilitate\ntraining in peer review and to provide\nappropriate recognition for Early Career\nResearchers who co-review manuscripts.\nReviewer 3\nReviewer’s comment Response\nI co-reviewed this manuscript with one of Thank you for your time and effort in\nthe reviewers who provided the listed helping to improve our paper.\nreports. This is part of the Nature\nCommunications initiative to facilitate\ntraining in peer review and to provide\nappropriate recognition for Early Career\nResearchers who co-review manuscripts.\nReviewer 4\nReviewer’s comment Response\nI thank the authors for their diligent\nresponses to my revisions and comments,\nand support the revised manuscript for Thank you so much for your valuable input.\npublication. It really helped in improving the paper.\nThe additional elaboration on the role in\nadvancing the frontier of AI-assisted\nsystematic maps, as well as our\nunderstanding of the field\nof CDR better highlight the novelty and\ncontribution of this work, and the addition of\nthe new Figure 2 emphasizes the large\nperimeter of this synthesis.\nI feel that the revised document presents a\nclear explanation of the results, and\nwell-supported interpretation and evaluation\nof their relevance. The authors made\nvaluable additions for interpreting the\naccuracy and precision of their findings by\nincluding the performance and confidence\nintervals of the classifiers, as well as\nincluded numerous statements that better\ndefine the limitations of interpreting certain\npatterns and trends (e.g. high growth rates\nat low publication numbers in Fig 3). The\nrevised section \"2.5 IPCC reports,\npolicy-making\nand practice have foci different from the\nscientific literature\" enriches the\ncomparison between the distribution of\nacademic literature\nand publications informing policy. With\nregards to methodology, my previous\nrequest to add clarity with regards to the\nprocess for named\nentity recognition has been addressed.\nReviewer 5\nReviewer’s comment Response\nThe authors have thoroughly revised the Thank you once again for taking the time to\nmanuscript, and I now recommend help improving our paper.\nacceptance given some minor revisions.\n### Feedback on the reply to reviewer We sincerely apologize if we gave the\nimpression that we do not value your\nI found the tone of the reply document less specific input - this was absolutely not our\nthan collegial and in places, replies intention. We deeply respect the scientific\nappeared directed more toward the editor peer review process and, from our own\nthan the experience, understand the significant effort\nreviewer. This impression stems from required to provide constructive and\nseveral observations: valuable feedback. We are truly grateful for\nthe time and effort you have dedicated to\n1. The authors repeatedly refer to other helping us improve.\nreviewers, which is irrelevant to my review\nprocess, as I had no access to their Thank you also for your detailed feedback\nfeedback. Replying to a comment is more on our comments. We appreciate your\nconvincing that referring to the judgment of dedication and will take your suggestions\nanother reviewer. into account in our future responses.\n2. Phrases like _\"this part has been deleted\non request by reviewers 1 and 4\"_ appear\ntwice in reply to my comments.\nAcknowledging reviewer 5’s feedback\nequally would have been appropriate.\n3. Statements such as _\"the decision\nregarding whether we should submit\nelsewhere ultimately lies with the editor\"_\nare unnecessary and\ndismissive. This is self-evident, but\nreviewers are expected to make\nrecommendations to the editor; I specifically\nused the term \"encourage.\"\nThe authors seem more annoyed than\nappreciative of the peer-review process.\nThis is disheartening given the voluntary\nnature of reviewing and the detailed\nfeedback provided in my first review, which\nincluded examples and actionable\ncorrections.\nThe authors note that they _\"generally do\nnot agree with this harsh judgment\"_\nregarding clarity issues but nonetheless\nimplemented most of my detailed\nsuggestions to improve the writing. This\ncontradiction suggests either a reluctance\nto accept critique or a lack of clarity in their\nrebuttal. Please either accept critique or\ndefend. This is more transparent.\nYour research is valuable, and ensuring that\nthe writing is as clear and precise as\npossible will increase its impact and reach. I\nsuggest that\nthe main authors consult the Nature guides\non writing ([link\n1](https://www.nature.com/articles/nphys72\n4\n<https://www.nature.com/articles/nphys724\n>), [link\n2](https://www.nature.com/articles/nmeth.45\n32\n<https://www.nature.com/articles/nmeth.453\n2>), [link\n3](https://www.nature.com/nature-portfolio/f\nor-authors/write\n<https://www.nature.com/nature-portfolio/for\n-authors/write>)), which\noffer helpful advice for improving clarity and\naccessibility in scientific writing. These\nguides include relevant tips such as\navoiding\n\"elegant\" variance and hyperbole. It was\nnice to see the authors removed most of the\nelegant variance from the initial submission\n(e.g.\nconsistently using \"CDR options\").\nLastly, on reference styles: The issue is not\nthat the manuscript was submitted in APA\nstyle, but that the referencing was done\ninconsistently. Defending this by noting that\nNature will standardize the references later\nis insufficient. As I noted in my initial review,\nsuch carelessness is inconsiderate to\nreviewers, who rely on clarity and\nconsistency to evaluate the manuscript\neffectively.\n###### Remaining comments\n1: In my first review I highlighted several We apologize for this mistake and do not\nmistakes that suggested carelessness and want to give the impression that we are\nrequested greater attention to internal careless. On the contrary, we try to address\nrevisions of the writing. That the main text all your comments.\nof the revised manuscript starts with a typo\nand a misplaced comma \"...reductions ,\nneed...\" is not reassuring. Such oversights\nare inconsiderate to reviewers, who\nvolunteer their time to improve the work.\nPlease be kind to your peers.\nThe authors report finding very little We believe there may be a\nresearch on BECCS, but this may result misunderstanding. We classify any\nfrom not including broader research on research as being on BECCS if it discusses\nbioenergy and CCS. While I have not CCS in conjunction with bioenergy as we\nverified this directly, I suggest that the have also fixed in the Coding Guideline. We\nauthors provide a brief reflection on this are confident that the finding of limited\npotential limitation in the discussion. research on BECCS accurately reflects the\nfact that comparatively little work has been\nconducted in this area.\nI didn't receive a reply to my request for a We do not report on ~70,000 articles\ncritical reflection on the stark difference in related to CDR but rather focus on ~29,000\nresults from the authors’ initial search query articles. This difference arises from a\n(~70,000 articles) compared to two earlier distinct search query and search strategy.\nreviews (2,900 and 6,200 articles in 2017 The mentioned previous approach did not\nand 2018, respectively). The 1,000% apply filtering after running the search\nincrease in five query. To ensure a high proportion of\nyears is striking and warrants exploration. A relevant documents (i.e., high precision)\ncomparison of search strategies or without additional filtering, they employed\nreapplying earlier queries to recent data highly restrictive search queries, which may\ncould offer have excluded some relevant documents\nalternative explanations for this difference.\n(resulting in low recall).\nIn this study, we adopted a different\napproach using machine learning to deploy\nan additional filtering step. Initially, we used\nbroader search queries to capture as many\nrelevant documents as possible (high\nrecall), even if this included many irrelevant\nones (low precision), resulting in the\n~70,000 documents you referenced. In the\nsecond step, machine learning was used to\nfilter out irrelevant documents, allowing us\nto achieve both high recall and high\nprecision.\nWe believe this process is already\nadequately explained in the first paragraph\nof the results section of the paper:\n“There is a much larger body of CDR\nresearch than previously suggested. Based\non our machine learning assisted approach\nthat enables us to identify CDR studies with\nhigh precision (0.88±0.0119, meaning the\nproportion of relevant studies among those\nidentified is high) and recall (0.93±0.005,\nindicating most relevant studies are\ncaptured) - see Methods, SI and Figure 1 -\nwe predict a total of 28,976±3800 scientific\nstudies in the Web of Science and Scopus\n(the two largest bibliographic core\ncollections). This is 3-4 times larger than\nwhat previous scientometric studies (J.C.\nMinx, Lamb, et al. 2017) or ongoing\ncommunity efforts to manually track CDR\nresearch (Burns 2021) have suggested\nwhen comparing the same time range. For\nthe former study, this discrepancy likely\narises from their reliance on non-machine\nlearning methods, which forced a\nhigh-precision, low-recall search approach.\nIn the case of the manual tracking efforts,\nthe rapid expansion of CDR literature has\nsimply made comprehensive tracking\nunfeasible.”\nWhile not a major issue, the authors We truly believe that tasks requiring little to\nprovided a lacking reply to my comment no intellectual or creative input should be\nstarting with \"In paragraph 8, it is argued\". automated whenever possible. The\nThey reply that 10.5 days per estimate of 10.5 working days per person is\nauthor/research assistant (=366 workdays just that—an estimate. In reality, no one can\nspread over 26 authors and 7 research sustain such work full-time. Personally, I\nassistants) for data collection would bean find it impossible to engage in this type of\nunwise use of their time. Many researchers, task for more than 1–2 hours a day.\nespecially those conducting fieldwork or\nexperiments, may find this perspective Comparing this kind of automation to\ndismissive. This is particular grating since fieldwork is not appropriate, as fieldwork\nyou have funding for 7 research assistants. inherently cannot be automated. We view\nLess arrogance is always appreciated. As a our stance not as arrogant but as a wise\nside note, AI investments reflect monetary allocation of intellectual resources.\nvalue to investors rather than social value,\ncontrary to the implication in the Moreover, automation offers the distinct\nmanuscript. advantage of being repeatable for newly\npublished work, making it highly scalable.\nWe have already addressed these points in\nour previous response and have adjusted\nthe text in the document accordingly after\nour first response.\nTo my comment starting with \"In paragraph Since we addressed the reviewers’\n3 it seems the authors aim to introduce a comments in order of their numbering, this\nlist of knowledge domains\", the authors section was deleted in a previous edit. We\nreply that it is unclear which paragraph is apologize for the inaccuracy in our earlier\nmeant. This is a lazy and disingenuous reply, which should have stated: “This part\nreply, since the third paragraph after the was deleted in a previous edit.”\n\"Introduction\" (in the original\nsubmission) header says \"CDR has been a\nkey part of climate change mitigation\ndiscussions in the scientific literature, but\nhas often been discussed in distinct\nknowledge domains\".\nThe abstract reads \"pointing towards We replaced that part with: “highlighting the\npotential inefficiencies at the science-policy differing development stages of CDR\ninterface\". First and minor, this is technologies.” to be consistent with the\nunnecessarily vague.\nrelevant paragraph on this in the discussion\nMore importantly, after comments by the section.\nreviewer(s), the authors admit throughout\nthe article that the number of studies should\nnot necessarily be mirrored in policy. It\nseems the authors have forgotten their own\nimprovement in this quoted sentence of the\nabstract.\nMinor:\n- \"stabilising warming at or below\" Why We agree. “At or below” emphasizes that it\nstabilize at 1.5°C if net-negative emissions is indeed possible to go below 1.5°C.\nare possible? This could be reconsidered,\neven if it was written as such in the most\nrecent IPCC.\n- \"deploy CDR versus CDR's required role\". We replaced “required” with “estimated”.\nThis sentence sounds like massive use\nCDR is required by the real-word, but it\nprimarily reflects the assumptions of model\nscenarios. Rephrasing could clarify this\ndistinction.\n- As noted earlier, a rate implies speed, and Thanks, we implemented this suggestion.\nan increasing rate implies acceleration.\n\"Ever increasing rates\" would represent the\nthird derivative (\"jerk\") in physics, which\nseems unlikely. \"Increasing rates\" would\nsuffice for clarity.\n- Most writing guides recommends avoiding We deleted “great” and “strong” in the given\nexaggerated phrases like \"great benefit to sentences.\nthe research community\" or \"a strong\nfocus,\" as they can weaken arguments\nrather than strengthen them.\nSection 2.3 finds that 30 % of all CDR We do not emphasize a conclusion that\nfirst-authors are from China, and 13 % is China is disproportionately\nfrom the US. The Chinese and US overrepresented. Actually, we would prefer\npopulations constitute 17 % and 4 % of the not to highlight this issue at all, as we don’t\nworld population, Without such context, know what a proportional representation of\nreaders may global research would look like, given that\nincorrectly conclude that China is countries have different capacities and\ndisproportionately overrepresented, research foci.\nwhen the opposite is true.\n\"our intuition suggests that this proportion\nshould be even greater\". This statement Thanks for this feedback. We have decided\nseems unfitting, particularly after to refer back to one of the IPCCs stated\nemphasizing that \"ought ≠ is.\" Additionally, goals, which is to comprehensively evaluate\nthe term \"this proportion\" is unclear and\nthe available scientific literature.\ncould benefit from more specificity.\nThus, we changed the previous version:\n“While the IPCC includes a relatively higher\nproportion of reviews (19% vs. 15%) and\nsystematic reviews (3% vs. 1%) compared\nto the overall CDR literature, our intuition\nsuggests that this proportion should be\neven greater to help capture and provide\naccess to the extensive uncited literature.”\nAs follows:\n“While the IPCC includes a relatively higher\nproportion of reviews (19% vs. 15%) and\nsystematic reviews (3% vs. 1%) compared\nto the overall CDR literature, we believe\nincorporating even more of these could\nfurther enhance its ability to fulfil a stated\ngoal of the IPCC - which is to\ncomprehensively evaluate the available\nevidence.”\n\"A stronger focus on scenarios is For clarity, we change it to: “The fact that\nsupported\". It is unclear whether this is a IPCC assessments have tended to focus on\nsuggestion or a description. Clarifying the scenarios is underlined by…”\nintended message would help readers.\n\"to be scaleable\". This phrasing is “This allows our systematic map to be\nconfusing if the benefit has already been scalable to the entire research domain\nrealized, as is the case, now that the study\naround CDR”\nis performed.\nis replaced by\n“This allows our systematic map to cover\nthe entire research domain around CDR”\n\"diverse range of CDR options and We replaced “interdisciplinary” with\ninterdisciplinary fields\". I apologise “multidisciplinary”.\nthat my initial comment was unclear. I\nmeant to note that interdisciplinarity is\ndifferent from multidisciplinarity. In the\nrevision, the authors make a valid\nargument, but it seems they may be\nreferring to multidisciplinarity (i.e.,\n\"researchers from different disciplines\")\nrather than interdisciplinarity. If this is not\nthe case, the phrasing is somewhat\nconfusing, and additional clarification would\nhelp ensure the intended meaning is clear\nto readers.\nTestament to the difficulty of clear writing, I We believe that the phrase “support\nwas also unclear in my comment on the role evidence-based decision-making” implies\nof social science in evidence-based policy. that decision-making is not necessarily\nWhen I said the humanities do not driven by the social sciences, but involves a\ncontribute to the evidence-base, it was not mutual exchange between science and\nto say that humanities are irrelevant or policy.\nwithout value, rather the opposite. The\nrevision says \"social sciences and We see no reason to distinguish the\nhumanities, for example to support humanities in this process, as even\nevidence-based decision-making\", but this interpretive evidence (for instance on the\nmisses my point. While the humanities offer different values implicit in policy pathways)\nvaluable insights, they do not contribute to can be valuable to clarify different positions.\nevidence-based policy because their Is it sufficient that we do not use the word\ncontributions are interpretive rather than “empirical” here?\nempirical. My intention was to highlight this\ndistinction, not to diminish the importance of\nthe humanities in broader decision-making\nprocesses."}, {"round": 3, "reviewer_comments": {"reviewer_5": "Dear Sarah Lück and co-authors\nThank you for your responses to my prior critiques, both about the tone of reply and the content.\nThe revised manuscript is clear, and let the findings speak on their own in modest language.\nMy remaining comments have been convincingly adressed, either by revision or fair rebuttals.\nIt was solid reseach, now its also a solid paper. Congratulations!"}, "author_response": ""}]

Decision letter