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Demand-side solutions to climate change mitigation consistent with high levels of well-being, , , , , , , , , and 16 other author(s). Nature Climate Change, 12 (1): 36--46 (November 2021)Tackling Climate Change with Machine Learning, , , , , , , , , and 12 other author(s). (2019)cite arxiv:1906.05433Comment: For additional resources, please visit the website that accompanies this paper: https://www.climatechange.ai/.Digitalization and the Anthropocene, , , , , , , , , and 11 other author(s). Annual Review of Environment and Resources, 47 (1): 479--509 (October 2022)Tackling Climate Change with Machine Learning., , , , , , , , , and 12 other author(s). ACM Comput. Surv., 55 (2): 42:1-42:96 (2023)Considering sustainability thresholds for BECCS in IPCC and biodiversity assessments, , , , , and . GCB Bioenergy, 13 (4): 510–515 (February 2021)Negative emissions—Part 1: Research landscape and synthesis, , , , , , , , , and 9 other author(s). Environmental Research Letters, 13 (6): 063001 (June 2018)Biophysical and economic limits to negative CO2 emissions, , , , , , , , , and 30 other author(s). Nature Climate Change, 6 (1): 42--50 (January 2016)Negative emissions—Part 3: Innovation and upscaling, , , , , , , , , and . Environmental Research Letters, 13 (6): 063003 (June 2018)Negative emissions—Part 2: Costs, potentials and side effects, , , , , , , , , and 8 other author(s). Environmental Research Letters, 13 (6): 063002 (June 2018)Tackling Climate Change with Machine Learning, , , , , , , , , and 12 other author(s). ACM Comput. Surv., (February 2022)