Publication
This study uses advanced machine learning algorithms to analyze the indicators influencing national climate ambition in 2030 emissions targets and net-zero pledges. By focusing on technical and social feasibility, and political credibility, we assess climate ambition of countries through cumulative per capita emissions from 2021 to the net-zero target year.
This study uses advanced machine learning algorithms to analyze the indicators influencing national climate ambition in 2030 emissions targets and net-zero pledges. By focusing on technical and social feasibility, and political credibility, we assess climate ambition of countries through cumulative per capita emissions from 2021 to the net-zero target year. Key indicators identified include: greenhouse gas (GHG) emissions per capita, share of renewable energy, total GHG emissions from hard-to-abate sectors, non-CO2 emissions per capita, energy intensity, oil and gas rents, press freedom, coal rents and average animal protein supply. Our model projects net-zero targets based on these national indicators, enabling a comparison between projected and actual net-zero targets. Notably, major emitting countries like Saudi Arabia, India, and Indonesia, could adopt more ambitious net-zero targets. The model also estimates net-zero targets for countries that have not yet set one. Extending the projected net-zero targets to all countries would further reduce global emissions by 22%, achieving levels of 62% below 2019 levels by 2050. These outcomes combined provide a comprehensive tool for evaluating and improving countries’ net-zero targets.
den Elzen, M.G., Weenk, S.C., Nascimento, L. et al. Climate ambition explained: key indicators and net-zero target projections using machine learning. Mitig Adapt Strateg Glob Change 31, 60 (2026)
https://doi.org/10.1007/s11027-026-10330-4