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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
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