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Negative emissions—Part 1: Research landscape and synthesis
Open accessAbstract
With the Paris Agreement's ambition of limiting climate change to well below 2 °C, negative emission technologies (NETs) have moved into the limelight of discussions in climate science and policy. Despite several assessments, the current knowledge on NETs is still diffuse and incomplete, but also growing fast. Here, we synthesize a comprehensive body of NETs literature, using scientometric tools and performing an in-depth assessment of the quantitative and qualitative evidence therein. We clarify the role of NETs in climate change mitigation scenarios, their ethical implications, as well as the challenges involved in bringing the various NETs to the market and scaling them up in time. There are six major findings arising from our assessment: first, keeping warming below 1.5 °C requires the large-scale deployment of NETs, but this dependency can still be kept to a minimum for the 2 °C warming limit. Second, accounting for economic and biophysical limits, we identify relevant potentials for all NETs except ocean fertilization. Third, any single NET is unlikely to sustainably achieve the large NETs deployment observed in many 1.5 °C and 2 °C mitigation scenarios. Yet, portfolios of multiple NETs, each deployed at modest scales, could be invaluable for reaching the climate goals. Fourth, a substantial gap exists between the upscaling and rapid diffusion of NETs implied in scenarios and progress in actual innovation and deployment. If NETs are required at the scales currently discussed, the resulting urgency of implementation is currently neither reflected in science nor policy. Fifth, NETs face severe barriers to implementation and are only weakly incentivized so far. Finally, we identify distinct ethical discourses relevant for NETs, but highlight the need to root them firmly in the available evidence in order to render such discussions relevant in practice.
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“Although there has been significant progress in negative emissions research [260,540,575,580,707], the actual CO 2 removal industry is still in its infancy.”
From Tackling climate change with machine learning (2023) · cites this paper as [540]
In the citing paper
“Current research on NETs has come from two main strands: technology-focused studies that explore specific technical configurations, and integrated assessment models that investigate NETs in the context of global and long-term climate change mitigation.[3][4][5]”
From The mutual dependence of negative emission technologies and energy systems (2019) · cites this paper as [4]
In the citing paper
“In fact, to meet current climate goals, the continued growth of emissions indicated by current trends will increasingly lock energy system transformations into pathways that require large-scale NETs. 4”
From The mutual dependence of negative emission technologies and energy systems (2019) · cites this paper as [4]
In the citing paper
“BECCS has been the first NET widely taken up in integrated assessment models 4 and shows some appealing characteristics.”
From The mutual dependence of negative emission technologies and energy systems (2019) · cites this paper as [4]
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Metadata
- Authors
- Minx, J. C., Lamb, W. F., Callaghan, M. W., Fuss, S., Hilaire, J., Creutzig, F., Amann, T., Beringer, T., de Oliveira Garcia, W., Hartmann, J., Khanna, T., Lenzi, D., Luderer, G., Nemet, G. F., Rogelj, J., Smith, P., Vicente Vicente, J. L., Wilcox, J., & del Mar Zamora Dominguez, M.
- Affiliations
- University of Leeds (GB)
- Mercator Research Institute on Global Commons and Climate Change (DE)
- Universität Hamburg (DE)
- Potsdam Institute for Climate Impact Research (DE)
- University of Wisconsin–Madison (US)
- International Institute for Applied Systems Analysis (AT)
- University of Aberdeen (GB)
- Colorado School of Mines (US)
- Corresponding author
- Minx, Jan C
- ORCID
- Venue
- Environmental Research Letters
- Publisher
- IOP Publishing
- Published
- 2018-05-21
- Volume
- 13
- Issue
- 6
- Pages
- 063001
- ISSN
- 1748-9326 (ISSN-L)
- OpenAlex
- W2803435903
- Cited by
- 865
- References
- 155
- Citations by year
- 2026: 68 · 2025: 113 · 2024: 125 · 2023: 141 · 2022: 134 · 2021: 115 · 2020: 82 · 2019: 71 · 2018: 16
- FWCI
- 41.61
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Environmental Research Letters (publishedVersion, cc-by)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- IIASA PURE (International Institute of Applied Systems Analysis) (acceptedVersion)
- Aberdeen University Research Archive (Aberdeen University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Max Planck Digital Library (submittedVersion, other-oa)
- Publication Database PIK (Potsdam Institute for Climate Impact Research (PIK)) (submittedVersion, other-oa)
- MPG.PuRe (Max Planck Society) (submittedVersion, cc-by)
- Spiral (Imperial College London) (submittedVersion, cc-by)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
- ETH Zürich Research Collection (cc-by)
- Funders
- UK Energy Research Centre (UKERC/FFR2/3)
- Bundesministerium für Bildung und Forschung (01LS1610B, 03EK3046B)
- Deutsche Forschungsgemeinschaft (DFG EXEC 177, DFG SPP 1689)
- Natural Environment Research Council (NE/P019455/1, NE/P019900/1)
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate Change and Geoengineering
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Climate Change Policy and Economics
- Physical Sciences › Chemical Engineering › Filtration and Separation › Carbon Dioxide Capture Technologies
- Keywords
- negative emissions technologies, climate change mitigation, Paris Agreement, 1.5 °C warming target, 2 °C warming limit, ocean fertilization, implementation barriers, climate policy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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