This dataset examines lay public perceptions of Carbon Dioxide Removal in the four devolved nations of the UK. We focus especially on 'biological' or 'nature-based' carbon removal techniques - perennial biomass crops, peatland restoration, and biochar. We also examine perceptions of trade-offs between options, and between carbon removal more generall versus emissions reduction. To deal with inevitable framing effects, we split the qualitative sample into two groups - one with a techno-economic framing mirroring current climate discourses, versus one 'everyday life' framing. We include deliberative workshop methods, providing both in-depth qualitative and small-n quantitative data, with large-n representative survey data. In addition, we explore the role of novel information devices - specifically Large Language Models (also known as Generative AI) such as ChatGPT as devices for deliberation, examining to what extent these support or disrupt the preceding discourses on carbon removal.This dataset examines lay public perceptions of Carbon Dioxide Removal in the four devolved nations of the UK. We focus especially on 'biological' or 'nature-based' carbon removal techniques - perennial biomass crops, peatland restoration, and biochar. We also examine perceptions of trade-offs between options, and between carbon removal more generall versus emissions reduction. To deal with inevitable framing effects, we split the qualitative sample into two groups - one with a techno-economic framing mirroring current climate discourses, versus one 'everyday life' framing. We include deliberative workshop methods, providing both in-depth qualitative and small-n quantitative data, with large-n representative survey data. In addition, we explore the role of novel information devices - specifically Large Language Models (also known as Generative AI) such as ChatGPT as devices for deliberation, examining to what extent these support or disrupt the preceding discourses on carbon removal.

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