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Making carbon pricing work for citizens
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“ML can also be used to predict the marginal emissions of energy consumption in real time, on a scale of hours [832], potentially allowing consumers to effectively schedule activities such as charging an EV when the emissions (and prices [439]) will be lowest [143].”
From Tackling climate change with machine learning (2023) · cites this paper as [439]
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Metadata
- Authors
- Klenert, D., Mattauch, L., Combet, E., Edenhofer, O., Hepburn, C., Rafaty, R., & Stern, N.
- Affiliations
- Mercator Research Institute on Global Commons and Climate Change (DE)
- University of Oxford (GB)
- CIRED (FR)
- University of Cambridge (GB)
- London School of Economics and Political Science (GB)
- Corresponding author
- Klenert, David
- Venue
- Nature Climate Change
- Published
- 2018-07-30
- Volume
- 8
- Issue
- 8
- Pages
- 669-677
- ISSN
- 1758-678X (ISSN-L)
- 1758-6798
- OpenAlex
- W2883135331
- Cited by
- 793
- References
- 84
- Citations by year
- 2026: 100 · 2025: 112 · 2024: 122 · 2023: 107 · 2022: 117 · 2021: 100 · 2020: 76 · 2019: 48 · 2018: 8
- FWCI
- 88.18
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Climate Change (publishedVersion)
- London School of Economics and Political Science Research Online (London School of Economics and Political Science) (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Publication Database PIK (Potsdam Institute for Climate Impact Research (PIK)) (submittedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Climate Change Policy and Economics
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Energy, Environment, and Transportation Policies
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate Change and Geoengineering
- Keywords
- carbon pricing, public acceptance, political acceptability, distributive fairness, public trust in government, policy stability, climate change mitigation, behavioral economics
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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