Machine learning map of climate policy literature reveals disparities between 
scientific attention, policy density, and emissions

Callaghan, Max; Banisch, Lucy; Döbbeling-Hildebrandt, Niklas; Edmondson, Duncan; Flachsland, Christian; Lamb, William F.; Levi, Sebastian; Müller-Hansen, Finn; Posada, Eduardo; Vasudevan, Shraddha; Minx, Jan C.

doi:10.1038/s44168-024-00196-0

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Machine learning map of climate policy literature reveals disparities between scientific attention, policy density, and emissions

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/persons/resource/max.callaghan

Callaghan, Max
Potsdam Institute for Climate Impact Research;

Banisch, Lucy
External Organizations;

/persons/resource/niklas.doebbeling-hildebrandt

Döbbeling-Hildebrandt, Niklas
Potsdam Institute for Climate Impact Research;

Edmondson, Duncan
External Organizations;

Flachsland, Christian
External Organizations;

/persons/resource/William.Lamb

Lamb, William F.
Potsdam Institute for Climate Impact Research;

Levi, Sebastian
External Organizations;

/persons/resource/mhansen

Müller-Hansen, Finn
Potsdam Institute for Climate Impact Research;

Posada, Eduardo
External Organizations;

Vasudevan, Shraddha
External Organizations;

/persons/resource/jan.minx

Minx, Jan C.
Potsdam Institute for Climate Impact Research;

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Citation

Callaghan, M., Banisch, L., Döbbeling-Hildebrandt, N., Edmondson, D., Flachsland, C., Lamb, W. F., Levi, S., Müller-Hansen, F., Posada, E., Vasudevan, S., Minx, J. C. (2025): Machine learning map of climate policy literature reveals disparities between scientific attention, policy density, and emissions. - npj Climate Action, 4, 7.
https://doi.org/10.1038/s44168-024-00196-0

Cite as: https://publications.pik-potsdam.de/pubman/item/item_31879

Abstract

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.