NGFS-IIASA Scenario Explorer

IIASA researchers collaborated with over 80 central banks and financial market supervisors from the Network of Central Banks and Supervisors for Greening the Financial System (NGFS) and the Potsdam Institute for Climate Impact Research (PIK) to establish a new set of climate scenarios. The updated climate scenarios are essential for future climate risk assessment, as they will improve climate stress tests that key central banks like Banque de France and the Bank of England plan to apply to their financial institutions in order to help create a more sustainable world economy.

The new scenarios indicate how early reductions in greenhouse gas emissions can minimize both financial and physical risk, while no action taken toward greenhouse gas emission reduction would drive up costs. The updated version of climate scenarios illustrates how changes to the global economy including significant investment flow toward clean energy are essential toward achieving a goal of net-zero emissions by 2050.

IIASA’s Energy, Climate, and Environment Program played a key role in providing the transition scenarios, as IIASA is the official host of the Scenario Explorer which is a web-based user interface that shows important data visualizations for NGFS transition scenarios. With the help of the Scenario Explorer, IIASA’s researchers effectively translated the predicted outputs of the Integrated Assessment Model for each scenario into time-specific data about predicted energy use and carbon emissions for each scenario on the national level.

The NGFS Climate Scenarios bring together a global, harmonised set of transition pathways, physical climate change impacts and economic indicators. The strength of the NGFS suite of models is in their global coverage and integrated assessment of risks. While significant research advances have been made recently, care should be taken in using the results, particularly at the most granular levels. Where possible, multiple models have been used for each scenario and warming level to represent uncertainty.