Long-Run Sectoral Transition Risk using a Hybrid MRIO/IAM Approach

Increasingly, process-based Integrated Assessment Models (IAM) have been used to project the long-term dynamics of the energy transition. However, as they are focused on modeling energy and land/water use, they have a limited understanding of the full spectrum of economic sectors, including service sectors, which account for half or more of GDP in many advanced economies. This paper introduces a flexible hybrid-LCA-style methodology to downscale IAM sectoral time series to a finer industrial and geographic level using a Multi-Regional Input/Output Table (MRIO). This method produces final energy projections that leverage the time series dynamics arising from the IAM’s bottom-up energy system modeling while providing sectoral granularity that accurately depicts indirect supply chain impacts captured by the MRIO. We then translate these measures to output with key elasticity parameters estimated using IEA and EU-KLEMS data. Using the example of the MESSAGEix-GLOBIOM, REMIND-MaGPIE, and GCAM IAMs and EXIOBASE, we generate projections under various transition scenarios across 50 countries and 40 sectors, and identify non-energy sectors with substantial transition downside risks along with sectors showcasing positive economic growth.