1.5°C Hotspots: Climate Hazards, Vulnerabilities, and Impacts

We have produced factsheets for Australia, China, the EU, India, Indonesia and USA showing 2030 and 2035 greenhouse gas emission reduction milestones for each jurisdiction to align with the Paris Agreement’s 1.5°C goal – both including and excluding land use, land-use change and forestry.

This study presents a new dataset of annual temperature projections for different overshoot pathways (when the 1.5 °C of global warming limit is exceeded). The data offers a unique opportunity to study local and regional climate change impacts of a range of overshoot scenarios, including when temperature thresholds might be exceeded, and by how much.

Despite an escalating climate crisis marked by unprecedented wildfires, storms, floods, and droughts, the Climate Action Tracker annual global temperature update shows global warming projections for 2100 are flatlining, with no improvement since 2021. The aggregate effect of current policies set the world on a path toward 2.7°C of warming.

The Climate Action Tracker provides updated 1.5ºC compatible benchmarks for the transport sector for the world as a whole and for seven individual countries; the US, EU, China, India, Brazil, South Africa and Indonesia.

Northern forests are critical in the race to net zero CO₂ by mid-century. Protecting and restoring these forests, alongside steep and rapid reductions in fossil fuel emissions, is essential both to mitigating emissions and to supporting forest ecosystem services and biodiversity.

This technical note describes the State of Climate Action series’ methodology for identifying sectors that must transform, translating these transformations into global mitigation targets primarily for 2030, 2035 and 2050 and selecting indicators with datasets to monitor annual change. It also outlines the report’s approach for assessing progress made toward near-term targets and comparing trends over time.

The LNG shipbuilding industry appears to be doubling down on building new carriers, ignoring the global shift to a low carbon economy and putting itself into an even worse oversupply situation than a year ago, we find in our updated report.

Seasonal forecasting of atmospheric parameters is important in various fields including agriculture. This study tests how machine learning can complement dynamic models using Tanzanian seasonal forecasts and finds this process uses far less computational power.