TY - JOUR A1 - Callaghan, Max A1 - Banisch, Lucy A1 - Doebbeling-Hildebrandt, Niklas A1 - Edmondson, Duncan A1 - Flachsland, Christian A1 - Lamb, William F. A1 - Levi, Sebastian A1 - Müller-Hansen, Finn A1 - Posada, Eduardo A1 - Vasudevan, Shraddha A1 - Minx, Jan C. T1 - Machine learning map of climate policy literature reveals disparities between scientific attention, policy density, and emissions JF - npj Climate Action N2 - Current climate mitigation policies are not sufficient to meet the Paris temperature target, and ramping up efforts will require rapid learning from the scientific literature on climate policies. This literature is vast and widely dispersed, as well as hard to define and categorise, hampering systematic efforts to learn from it. We use a machine learning pipeline using transformer-based language models to systematically map the relevant scientific literature on climate policies at scale and in real-time. Our “living systematic map” of climate policy research features a set of 84,990 papers, and classifies each of them by policy instrument type, sector, and geography. We explore how the distribution of these papers varies across countries, and compare this to the distribution of emissions and enacted climate policies. Results suggests a potential stark under-representation of industry sector policies, as well as diverging attention between science and policy with respect to economic and regulatory instruments. Y1 - 2025 U6 - https://doi.org/10.1038/s44168-024-00196-0 SN - 2731-9814 VL - 4 PB - Springer Science and Business Media LLC ER -