The work explores the issue of calculating uncertainty and identifying effects of the inputs of the complex models, integrating economic and climate systems, that we use to understand and address climate change. These models, known as Integrated Assessment Models (IAMs), predict many outputs. The main ones are usually CO2 emissions, and their future levels. This information is fundamental in designing climate policies. The authors focus on analysing these models using a method called Global Sensitivity Analysis (GSA). This method helps in identify which factors most influence the outcomes of the models.

The study investigates the role of  five key factors: energy efficiency, fossil fuel availability, economic growth, technology development, and population growth. These factors are examined under different scenarios, collegially designed by the scientific community, and called Shared Socioeconomic Pathways, to see how changes in each one affect CO2 emissions over time. The researchers find that economic growth and energy efficiency are the most significant factors influencing emissions. Interestingly, the availability of fossil fuels only matters in some scenarios.

One of the study’s key insights is that the importance of these factors changes over time, which means that policies need to adapt as circumstances evolve. For example, economic growth might be more important in the near term, while technology development could be crucial later on.

In order to discover these effects, the paper introduces a new approach to GSA that handles complex, time-varying data better than traditional methods. This new approach helps provide a clearer picture of how different factors interact and change over time, offering valuable information for policymakers.

In summary, this research improves our understanding of how different factors impact CO2 emissions and highlights the need for flexible, adaptive policies to effectively combat climate change.