Industry is modelled using technology-rich stock accounting simulation models that cover three energy-intensive sectors (chemicals and petrochemicals, pulp and paper, and aluminium) and TIMES-based linear optimisation models for the two remaining energy-intensive sectors (iron and steel, and cement). The five sub-models characterise the energy performance of process technologies from each of the energy-intensive sub-sectors, including 39 countries and regions. Typically, raw materials production is not included within the boundaries of the model, with the exception of the iron and steel sector in which energy use for coke ovens and blast furnaces is covered. Due to the complexity of the chemicals and petrochemicals sector, the model focuses on five products that represent about 47% of the energy use of the sector: ethylene, propylene, BTX (benzene, toluene and xylene.), ammonia and methanol.
Demand of materials is estimated based on country- or regional-level data for gross domestic product (GDP), disposable income, short-term industry capacity, current materials consumption, regional demand saturation levels derived from historical demand intensity curves, and resource endowments (Figure A.4). Total production is simulated by factors such as process, age structure (vintage) of plants and stock turnover rates. Overall production is similar across scenarios, but means of production differ considerably. For example, the same level of crude steel production is expected in both the 6°C Scenario (6DS) and the 2DS, but the 2DS reflects a much higher use of scrap (which is less energy-intensive than production from conventional raw materials).
Each industry sub-model is designed to account for sector-specific production routes for which relevant process technologies are modelled. Industrial energy use and technology portfolio for each country or region are characterised in the base year based on relevant energy use and material production statistics for each industrial energy-intensive subsector. Changes in the technology and fuel mix as well as efficiency improvements are driven by exogenous assumptions on penetration and energy performance of best available technologies (BATs), constraints on the availability of raw materials, techno-economic characteristics of the available technologies and process routes and assumed progress on demonstrating innovative technologies at commercial scale. Thus, the results are sensitive to assumptions on how quickly physical capital is turned over, relative costs of the various options, and on incentives for the use of BATs for new capacity.
The industry model allows analysis of different technology and fuel switching pathways in the sector to meet projected material demands within a given related CO2 emissions envelope in the modelling horizon.
Figure A.4 Structure of the industry model