Compounding Uncertainties in Economic and Population Growth Increase Tail Risks for Outcomes Across Sectors
Two critical factors that drive the Global Change Analysis Model (GCAM) are GDP and population, which themselves are related. This study models multiple distributions of future GDP and population trajectories representing different amounts of interaction between them to begin to quantify the value of correctly capturing the feedbacks and compounding effects between these important influences in GCAM. These distributions are used to drive GCAM and outcomes across sectors and regions are examined. This research highlights the amplified risks of extreme outcomes, such as higher food prices or water shortages, when uncertainties in future population and economic growth are considered together. Put simply, when specific feedback scenarios are accounted for rather than when sensitivity-test style independent distributions are assumed.
This study examines the global effects of different relationships between GDP and population changes. The multisectoral nature of GCAM makes it an excellent tool to investigate the magnified risks arising from the interaction of uncertainties across sectors such as energy, water, land, and emissions. This novel approach considers the compounding effects of these uncertainties, offering a more comprehensive view of potential extreme future scenarios that may be missed if GDP-population correlations are not accounted for.
This research explores the impact of uncertainties in economic and population growth on long-term multisectoral outcomes, utilizing the Global Change Analysis Model (GCAM). We find that uncertainties in GDP and population growth compound, leading to magnified tail risks across various sectors such as energy consumption, water withdrawal, staple food prices, and CO2 emissions. This magnification is particularly pronounced in the upper tail at both global and regional levels, indicating a greater risk of extreme outcomes when accounting for correlations between GDP and population drivers. Importantly, we find that the effects of compounded uncertainties are not uniform across regions. For instance, Western Africa and India exhibit significant variability in staple food prices due to higher population growth uncertainty. We also investigate an alternative scenario that GCAM can be run under, finding that while it is possible to constrain uncertainty in particular sectors by scenario design (e.g., final energy consumption and CO2 emissions), other sectors such as land and water see higher levels of uncertainty, as well as more extreme outcomes.
The findings highlight the importance of considering multisector and multisystem interactions between uncertainties across different regions. Accounting for these compounding uncertainties provides valuable insights into potential risks, which are vital for long-term planning in our interconnected human-Earth system.