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“The literature on city-scale mitigation strategies is also strongly biased towards the Global North [466], while key mitigation challenges are expected to arise from the Global South [570].”
From Tackling climate change with machine learning (2023) · cites this paper as [466]
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Metadata
- Authors
- Lamb, W. F., Creutzig, F., Callaghan, M. W., & Minx, J. C.
- Affiliations
- University of Leeds (GB)
- Mercator Research Institute on Global Commons and Climate Change (DE)
- Corresponding author
- Lamb, William F.
- Venue
- Nature Climate Change
- Published
- 2019-03-18
- Volume
- 9
- Issue
- 4
- Pages
- 279-287
- ISSN
- 1758-678X (ISSN-L)
- 1758-6798
- OpenAlex
- W2921017284
- Cited by
- 163
- References
- 65
- Citations by year
- 2026: 11 · 2025: 18 · 2024: 26 · 2023: 24 · 2022: 23 · 2021: 31 · 2020: 22 · 2019: 8
- FWCI
- 8.91
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Climate Change (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Publication Database PIK (Potsdam Institute for Climate Impact Research (PIK)) (submittedVersion)
- Topics
- Physical Sciences › Environmental Science › Environmental Engineering › Urban Heat Island Mitigation
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate Change and Sustainable Development
- Physical Sciences › Environmental Science › Global and Planetary Change › Sustainability and Climate Change Governance
- Keywords
- urban climate change mitigation, case studies, literature mapping, urban typology, climate policy, scientometrics, machine learning
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