Improving steel demand modeling in Integrated Assessment Models

Abstract

Steel production emits a large share of a country’s greenhouse gas emissions. Therefore it is important that the steel production is accurately modeled in Integrated Assessment Models (IAMs). This thesis focusses on using physical drivers, rather than GDP, in a method to model steel demand. These drivers are based on the two largest steel consuming end-use sectors: construction and automotive. Within those sectors, 9 drivers for steel demand were identified. After statistical analysis, only population, new dwellings constructed, total floor area and motor vehicles produced showed strong correlations with their sectors’ steel consumption. These four drivers were then used to model the steel demand for the entire end-use sector. The method used for modeling is a bottomup model based on population size and historical trends of steel intensity and product intensity.