Mapping climate mitigation policy literature using machine learning: disparities between scientific attention, policy density, and emissions

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. Our living map of climate policy research is publicly available and may both usefully support upcoming climate change assessments such as those by the Intergovernmental Panel on Climate Change (IPCC) and serve as a useful resource for the climate policy research community.

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

“…To classify governance and policy discussions we code the title and abstracts of the documents according to a categorisation scheme inspired by the Grantham Research Institute (2022) and the New Climate Institute (2020), but formalised by Callaghan et al (2024). Adjusting this scheme, we describe three levels of policy effort covering 19 individual codes, as shown in Fig.…”