COVID-related work

A collection of community work on the effects of the COVID-19 pandemic on emissions, energy demand and mitigation pathways, assembled during 2020 and 2021.

This web section serves as a hub to share news on COVID-related research by integrated assessment modeling teams. It collects COVID-related news items reported by IAMC members. Each news item consists of a title, a short overview and a link to more information on the website of the associated research project or research institution. If you would like to report a news item to be featured on this website, please contact the IAMC secretariat.

Background

In 2020 the consortium collected work from across the community on what the pandemic meant for emissions pathways: the size and duration of the emissions dip, the effect of recovery packages on investment, and whether behavioural changes in mobility and work would persist.

This page is kept as a record of that effort. It is not being actively extended.

30 July 2026

Reducing material requirements while decarbonising the Spanish economy: from a green growth to a postgrowth paradigm

The transition to a low-carbon economy is expected to increase material requirements, as low-carbon technologies (LCTs) typically require more materials than fossil fuel-based energy systems. Here, we extend the MEDEAS-Spain Integrated Assessment Model (IAM) to improve the representation of material requirements, stocks, and flows. Key novelties are: (i) the use

29 July 2026

Climate ambition explained: key indicators and net-zero target projections using machine learning

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.

28 July 2026

A harmonised dataset for Earth system foundation models

Foundation models for Earth systems have so far been trained primarily on physical climate and weather data, with limited representation of the human systems that both drive and respond to environmental change. The lack of a unified global training resource that combines climate, land, ocean, cryosphere, infrastructure, hazards, and socioeconomic