Expert:Energy demand - Steel

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Iron & Steel

Key publications

Van Ruijven et al., 2016:
B. J. Van Ruijven, D. P. Van Vuuren, W. Boskaljon, M. L. Neelis, D. Saygin, M. K. Patel (2016). Long-term model-based projections of energy use and CO2 emissions from the global steel and cement industries. Resources, Conservation and Recycling, 112, pp. 15-36, doi:

Neelis et al., 2006:
Neelis, M.L., Patel, M.K. (2006). Long-term production, energy use and CO2 emission scenarios for the worldwide iron and steel industry, UU CHEM NW&S (Copernicus), URL:

Van Sluisveld et al., 2021:
Mariësse A.E. van Sluisveld, Harmen Sytze de Boer, Vassilis Daioglou, Andries F.Hof, Detlef P.van Vuuren (2021). A race to zero - Assessing the position of heavy industry in a global net-zero CO2 emissions context. Energy and Climate Change, 2, doi:

Steel sector

Overall layout

Value chain representation

Available value chain elements represented in the IMAGE model:

Elements Presence Elaboration
Material extraction - -
Material demand x Logistic growth model plotted to historical per capita steel consumption data and per capita GDP (Neelis et al., 2006)
Production routes x Primary and secondary (van Ruijven et al., 2016)
Technology choice x Market share decided on multinomial logit formulation, choosing on relative production costs per region (van Sluisveld et al., 2021)
Trade x Trade is calculated based on the average regional production cost (e.g. production costs, transport cost and trade barriers) (van Sluisveld et al., 2021)
End use representation x Buildings, Machinery, Cars, Packaging.

Stock representation based on normal distribution using average lifetime and deviation (van Ruijven et al., 2016)

End-of-life representation x Recycling (van Ruijven et al., 2016)

Historical representation

Available capital stock present at the start of the simulation:

Fossil based capacity
  • BF/BOF Standard (Blast Furnace/Basic Oxygen Furnace)
  • Scrap EAF (Electric Arc Furnace)
  • DRI/EAF (Direct Reduced Iron/Electric Arc Furnace)

Material and energy demand

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Demand curves for steel industry (source: van Ruijven et al., 2016)

In IMAGE, the demand for crude steel is modelled by deriving a relationship from historical per capita steel consumption data and per capita GDP, and extending this into the future (See Figure 3).  The statistical model, reflecting a logistic growth curve with a decay factor (see Neelis and Patel (2006) for a more detailed description), is plotted through historical steel production data taken from the Iron and Steel Statistical yearbooks of 1980 (1970-1979), 1990 (1980-1989), 2000 (1990-1999) and 2001, 2002, 2003 and 2004. Historical population data is derived from UN statistics, and GDP from the World Bank (2010). The derived curve provides indication of the ‘intensity of use’ (IU) for steel specified per world region in the IMAGE model. Combined with the long-term population and GDP projections, which are exogenous trends to the IMAGE model (See Chapter 4), it provides a (static) estimate for crude steel that extends into the future.   

Total energy demand for crude steel production in the IMAGE model is derived from multiplying the total physical production volumes with specific energy consumption values as reported in literature (Van Ruijven et al., 2016a). A further breakdown of energy demand across the included production technologies and energy carriers is described in the next section.

  1. Deduct relationship steel consumption and economic activity (GDP/cap) (curve fitting through data, usually an S-shaped curve to impose a growth limit).
  2. A submodel takes into account dynamics such as trade, production stock turnover, material recycling, and competition between different steel and cement production technologies.
  3. This demand relationship (formula) can then be used. By multiplying with (1) total population per region, we can calculate the total material demand. Following with a multiplication of total demand with practice energy intensity values for steel making (values in GJ/tonne steel), we can deduct total energy demand [per region, and other subsets].
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Conceptual model of the steel sector


In IMAGE, it is assumed that the demand for steel is being met by either domestic production or trade with other regions. This means that production by region is determined by the projected demand and steel trade between regions. The main drivers of trade between regions are the relative production costs per region, the transport costs between the main ports of the two regions, and a trade barrier factor between regions based on historic trade data and scenario assumptions (e.g. increased or decreased openness of economies).

Production routes

The allocation of energy carriers and production technologies takes place in a two-step approach:

  • Step 1 determines how much of what energy carrier is required for each production technology (in cases of multiple uses)
    • Choice of energy carrier is dependent on energy price.
  • Step 2 determines the market share of production technologies. For each separate production line, the following information is gathered:
    • Total energy cost (sum of step 1)
    • Annualized investment costs
    • Annualized O&M cost
    • Other costs


Trade is calculated based on the average regional production cost (e.g. production costs, transport cost and trade barriers).


A special scrap steel module is made for the TIMER/IMAGE model, taking into account the lifetime of steel scraps in society (fast moving to locked scrap), steel characteristics (90% of secondary steel), recycling rate limitations (70%). Scrap is not traded internationally due to lack of data.

Dynamic options for systems change

The following capital stock alternatives are at the disposal of the IMAGE model. Investments shares are determined using a multinomial logit equation, giving larger market shares to cheaper technologies while still including some heterogeneity. Relative price differences are created through differences and changes in CAPEX, OPEX , carbon storage costs and policy costs over the time horizon of the model.

Class Option
Energy innovations
  • DRI/EAF (Direct Reduced Iron/Electric Arc Furnace)
  • SR/BOF (Smelting Reduction/Basic Oxygen Furnace)
  • TGR BF/BOF (Top-gas Recycling Blast Furnace/Basic Oxygen Furnace)
  • TGR BF/BOF BAT (Top-gas Recycling Blast Furnace/Basic Oxygen Furnace Best Available Technology)
  • Fuel Switching
  • BF/BOF Standard + CCS (Carbon Capture and Storage)
  • SR/BOF + CCS
Material innovations
  • EAF scrap
Process innovations
  • Electrowinning/EAF

Additional imposable options for systems change

The following options for systems change can be imposed onto the model. Not included in a standard IMAGE model scenario run.

Class Option
Energy innovations
  • Lifetime extensions
  • Material efficiency improvements
Material innovations
  • Lifetime extensions
  • Feedstock substitution
  • Material demand reduction
  • Recycling rate
Process innovations