Expert:Energy demand - Non-Energy

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Non Energy

Key publications

Daioglou et al., 2014:
V. Daioglou, A. P. C. Faaij, D. Saygin, M. K. Patel, B. Wicke, D. P. Van Vuuren (2014). Energy demand and emissions of the non-energy sector. Energy and Environmental Science, 7(2), pp. 482-498, doi:
Link to PBL-website:

Daioglou et al., 2015:
V. Daioglou, B. Wicke, A. P. C. Faaij, D. P. van Vuuren (2015). Competing uses of biomass for energy and chemicals: Implications for long-term global CO2 mitigation potential. GCB Bioenergy, 7(6), pp. 1321-1334, doi:

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:

Non-energy sector

Overall Layout

Value chain representation

Available value chain elements represented in the IMAGE model:

Elements Presence Elaboration
Material extraction - -
Feedstock demand x Logistic growth model plotted to historical per capita steel consumption data and per capita GDP (Daioglou et al., 2014)
Feedstocks x
  • Naptha
  • Methanol
  • Ethanol
  • Ethane
  • Biomass
  • Secondary streams

(van Sluisveld al., 2021; Daioglou et al., 2014)

Conversion routes x
  • High value chemicals (HVC, ethylene, propylene, xylene)
  • Ammonia
  • Methanol
  • Refinery products (lubricants, aromatics (BTX) and bitumen)

(Daioglou et al., 2014)

Technology choice - -
Trade - -
End use representation - -
End-of-life representation x Chemical and mechanical recycling, carbon looping (van Sluisveld et al., 2021)

Historical representation

Available capital stock present at the start of the simulation:

Fossil based capacity
  • Coal to Methanol
  • Coal to Naptha
  • Natural Gas to Ethane
  • Oil to Naptha


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Demand curves for non-energy products (Source: Daioglou et al. 2014)

In the IMAGE model, total demand for chemical products is based on a historical relation between the consumption of chemical products per capita and GDP per capita. A logistic growth curve is plotted through historical production data of the chemical industry from the Methanol Institute (1999-2003), OGJ (1997-2012a), OGJ (1997-2012b), and USGS (1996-2012) to derive an ‘consumption per capita’ formulation. By combining this curve with long-term population and GDP projections, which are exogenous trends to the IMAGE model, it provides an (static) estimate for chemical products that can extend into the future. This method yields regionally distinct demand curves for four aggregated chemical (intermediate) product groups (High value chemicals, ammonia, methanol and refinery products) without further detail on the end-use sectors. Specifically for ammonia, part of the demand is linked to the agricultural production module of IMAGE, thus accounting for changes in the demand of N-based fertilizer. 

Total energy demand for chemical products in the IMAGE model is derived from multiplying the total physical production volumes with specific energy consumption values as reported in literature. A further breakdown of energy demand over the included production technologies and energy carriers is described in the next section.

  • Total non-energy use = total production volume * specific non-energy use energy consumption (per region and ‘product’)
    • Total production is derived from data sources on production capacity for steam cracking and refinery products
    • Specific non-energy use and process energy is derived from literature describing global averages in the year 2000 (Weiss et al., 2000)
  • Production capacity is assumed to have a utilization rate of 90%.
  • The future projections of the non-energy products are driven by exogenous economic, population and fuel price developments
  • Production Intensity (GJfinal/cap) is related to total socio-economic change (GDP/cap) over time, for which a logistic growth relationship is extracted (s-curve fitting)
  • Final Energy Demand is considered to be dependent on energy price, for which a price elasticity for demand is implemented which puts restraint on demand growth
  • To consider the total need for primary energy, conversion efficiencies are used per feedstock product to calculate the need for primary energy to produce the final end products

Supply/Supply Chain

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Conceptual Model for the Non-Energy Sector


The IMAGE model mostly includes a representation of the upstream supply chain (primary-to-intermediate and intermediate-to-product) using conversion efficiencies (product and intermediate), annualized variable costs, and annualized fixed cost. These data are combined to determine the allocation of energy carriers to the production of product or intermediate.

  • To calculate how much (primary) energy demand needs to be supplied to fulfil demand, the model uses a 2-step approach (primary-to-intermediate and intermediate-to-product) using the following information:
    • Conversion efficiencies per step (product and intermediate) (literature search)
    • Variable costs per step (annualized):
      • e.g. Costs of fuels (endogenously calculated in the TIMER model)
      • Revenues from electricity generation
      • Other context “cost” parameters such as a carbon tax, premium factor etc.
    • Fixed cost per step (annualized):
      • Cost of specific production routes (deducted from literature, e.g. Ren et al (2009) and relevant databases)
  • Abovementioned data will be combined to determine the allocation of energy carriers to the production of product or intermediate (a share). Historical data is used to calibrate the outcome (match with history).


With regard to the downstream supply chain, in IMAGE recycling is based on assumptions regarding maximum recycling rates, for which 50% is assumed for HVC, 20% for methanol and 30% for refinery products. A distinction is made for mechanical recycling (max 30%) and Back-to-Feedstock (BfT, or chemical) recycling (70%).

  • Some of the final end product is assumed to be used as feedstock again (recycling). Assumptions are made on the max recycling rate, for which 50% is assumed for HVC, 20% of methanol and 30% for refinery products. Distinction is made for mechanical recycling (max 30%) and Back-to-Feedstock recycling (70%).
  • Waste incineration is also included, allowing some energy recovery that lowers the electricity demand of the non-energy sector.  Energy recovery occurs with a thermal efficiency of 30% (and 40% over time). Waste incineration is not economically in competition with recycling options.
    • Embedded carbon is assumed to remain accumulated unless incinerated.
    • Emissions from this model are therefore mostly processing emissions and some emissions from incineration

Dynamic options for systems change

The following capital stock alternatives are at the disposal of the IMAGE model. Options are chosen on a least-cost base. Relative price differences are created through differences and changes in CAPEX, OPEX and policy costs over the time horizon of the model.

Class Option
Energy innovations
Material innovations
Process innovations
  • Biomass to Ethanol/Methanol
  • Biomass to Naptha

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
  • CO2 to Methanol
Material innovations
  • Waste to Methanol
Process innovations
  • Black Liquor to Methanol