Global Tracking of Climate Change Adaptation Policy Using Machine Learning: a Systematic Map Protocol

Abstract Background — Countries around the globe have started implementing policies to respond to the current and future risks of climate change. The scientific literature on these adaptation policies is fragmented and no central typology is generally accepted, making tracking of global adaptation policy progress difficult.Methods — In this protocol, we describe how we use machine learning methods to classify scientific literature on adaptation policies following the ROSES guidelines. We use a broad search query in Scopus, MEDLINE and Web of Science (up to November 2021). We manually classify a subset of the documents and use this to train multiple supervised machine learning algorithms, including a state-of-the-art algorithm based on BERT. The classification scheme is aimed at providing a multi-functional database: we classify first based on a newly created typology, which is based around the well-established NATO categories of policy instruments; this is supplemented with categories on the types of impacts, evidence on maladaptation, constraints, evidence type, governance level and geographic location.Expected results – Using the typology and categories, as well as topic modelling, we create an overview of scientific literature on adaptation policies. This describes the breath of policy options, their geographic distribution, developments over time, and under-explored areas. If successful, this would result in the most comprehensive evidence map of adaptation policies to date; building on this, the machine learning algorithms and underlying data can serve as a basis for a living evidence map, moving towards the real-time tracking of adaptation progress.