Governance of carbon dioxide removal (CDR): an AI-enhanced systematic map of the scientific literature

Sarah Lück, Anna Mohn,William F. Lamb

crossref(2024)

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Abstract
For limiting global warming to well below 2°C rapid and stringent GHG emissions reductions are required. In addition, we also need to actively remove CO2 from the atmosphere via carbon dioxide removal (CDR). This will require advances in policymaking and governance to incentivise, coordinate and regulate CDR, including strict monitoring to ensure durable, additional removals that do not compete with emission reduction efforts. While it is critical to learn from the existing evidence on CDR policy and governance, there is no overview of this dispersed body of literature right now. IPCC and other science assessments have therefore treated the subject very selectively. This work addresses this lack of overview by systematically mapping the literature assessing policy and governance dimensions of CDR. Systematic mapping provides a comprehensive view of a research field by analysing the state of evidence, i.e. how much research is available at any point in time on which topics and geographies studied by whom, when and where. We use an AI-enhanced approach to systematic mapping, trimming down an initial set of about 30,000 documents on CDR to a set of 876 that deal with governance and policy issues. Our findings show sharply growing attention to CDR policies and governance issues over time, but with limited coverage of the Global South. Long established conventional CDR methods such as afforestation dominate the literature - particularly in ex-post studies - with little coverage of many novel CDR methods, such as biochar or direct air carbon capture and storage. We observe a shift from an initial discussion on CDR in international agreements towards the planning and implementation phase of national and sub-national policies. Our map can help to inform upcoming science assessments with critical information around CDR policies and governance and might serve as a starting point for generating a rigorous knowledge base on the topic in the future.
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