# Demand sectors :::{note} **In brief** — This chapter describes the end-use demand modules of OPEN-PROM: industry and domestic demand, the ICT / data-centre sub-sector split off during PRISMA, transport and bunkers, and the carbon dioxide removal (CDR) technologies. Each module turns economic activity into final energy demand by fuel and feeds the supply modules. ::: (industry-demand)= ## Industry and domestic demand The Industry and Domestic Demand module covers final energy consumption in the industrial sector and in the domestic sector (households and commercial/services, often grouped with residential demand). It projects how energy demand evolves in these sectors across the model horizon, disaggregating key subsectors and end-uses to capture structural and technological differences. Industry in OPEN-PROM is split into ten subsectors, reflecting a broad range of industrial activities with distinct energy profiles. These include energy-intensive industries such as: - Iron and Steel - Chemicals - Non-ferrous Metals (e.g. aluminium) - Non-Metallic Minerals - Pulp and Paper - Food, Drink and Tobacco - Engineering - Textiles - Other Industrial Sectors (covering remaining manufacturing sectors) By modelling these subsectors separately, the module can account for differences in production growth, technological options, and energy intensity. Each subsector's output or activity is usually driven by exogenous assumptions linked to GDP or sectoral value-added growth (for example, Iron & Steel demand may follow construction and manufacturing trends). The domestic part of the module includes three main subsectors: Services and Trade; Households; and Agriculture, Fishing, Forestry etc. These domestic demands are driven by indicators such as population, number of households, floor area of buildings, income levels, and urbanisation. Both the industry and domestic sub-modules use these drivers to estimate baseline activity growth, then determine energy consumption based on the technology and fuel choices available. The core of this module's functionality is the calculation of final energy demand by fuel for each subsector, using energy intensities or energy-service demand approaches. For example, in industry the model might have an energy intensity (energy per unit of output) that improves over time due to technological advances and efficiency policies; these improvements can be exogenous or endogenous (e.g. in response to energy prices). According to future activity projections, the module computes the total energy required to satisfy the demand of the specific sector. A fuel allocation routine then determines how that energy requirement is met by different fuels or technologies. The fuels considered usually include coal, oil, natural gas, biomass, hydrogen, electricity, and others. The module uses econometric formulations (such as logit or substitution elasticities) to split demand among fuels based on their relative prices and availabilities. For instance, if electricity becomes cheaper or cleaner, the model will shift some heating demand from fossil fuels to electric boilers or heat pumps in the residential sector (fuel switching); similarly, industries might adopt electric arc furnaces (using electricity) over blast furnaces (using coke for steelmaking) if conditions favour that, or switch from oil-fired furnaces to gas. The inclusion of hydrogen as a final demand option is a distinctive feature, allowing exploration of sector coupling and new fuel adoption. Thus price signals from the Prices module (energy prices by fuel) play a role in this module's equations, influencing the chosen fuel mix in each subsector via behavioural parameters or cost comparisons. On the domestic side, a similar logic applies to household energy uses: the module simulates how heating is supplied by gas boilers versus electric heat pumps versus possibly hydrogen boilers, how cooking shifts from LPG to electricity, or how insulation and efficiency improvements reduce overall energy needs. Many of these changes over time can be driven by exogenous policies (such as building codes mandating efficiency, or incentives for heat pumps) or by the model responding to price changes (e.g. high carbon prices making fossil heating more expensive, thus accelerating electrification). The outcome of the Industry and Domestic Demand module is a detailed breakdown of energy demand by sector, subsector, end-use, and fuel in each region and year. These demands are then passed to other modules: electricity demand and hydrogen demand computed here become inputs for the Power and Hydrogen Supply modules respectively, while demand for fossil fuels (gas, oil, coal) interacts with the Prices module to determine if supply is adequate or if price adjustments are needed. In turn, the module receives information on final energy prices (which incorporate primary energy prices and any taxes or subsidies) from the Prices module each iteration, to recalculate consumption with price-responsive elements. When run standalone, the Industry/Domestic module uses exogenous price trajectories to simulate energy demand; within the integrated model it provides a crucial link from economic activity to energy usage, and ultimately to emissions. :::{seealso} Electricity and hydrogen demands computed here feed the supply side — see {ref}`power-supply` and {ref}`hydrogen-supply`. ::: ## Residential and commercial :::{note} Despite its heading, this sub-section documents the **ICT / data-centre** sub-sector, which was split off from the services sector during the PRISMA project to track the fast-growing electricity consumption of data centres. ::: The ICT (Data Centres and related Infrastructure) sector is separated from the services sector, during the PRISMA project, to create a new sector that keeps track of the final consumption of the newly merged data-centres sector, which is expected to have significant growth over the coming years. For the projection of data-centre electricity demand we fit an econometric curve based on the IEA short-term projections, with further analysis leading to the creation of different scenarios (Lower, Medium and Higher Usage). (transport-demand)= ## Transport and bunkers demand The Transport Demand module models energy demand in the transportation sector by distinguishing between passenger and freight transport modes. In OPEN-PROM, transport is disaggregated into eight categories (modes) to reflect differences in technologies and usage patterns. Passenger transport includes passenger cars, passenger rail, aviation, buses, and passenger inland navigation, while freight transport comprises freight trucks, freight rail, and freight inland navigation. International maritime and aviation activities are treated separately under the bunkers category, outside the main transport module. This level of disaggregation, produced in the context of DIAMOND, allows the model to capture the distinct characteristics of each transport mode. For instance, passenger cars and aviation differ significantly in their energy consumption patterns, operational behaviour, and available fuel options. The module projects transport activity growth based on key socio-economic drivers. Activity indicators are expressed as: - Kilometres per year for passenger cars - Passenger-kilometres for other passenger modes - Tonne-kilometres for freight transport In addition, the total passenger car stock in each region is estimated using socio-economic indicators such as GDP per capita and population. Rising incomes and urbanisation generally lead to increased demand for passenger mobility, reflected in higher car ownership rates and greater air travel. Similarly, growth in economic output and trade stimulates demand for freight transport services. A core component of the Transport Demand module is the representation of vehicle technologies and fuel choices, particularly in road transport. The passenger car segment is modelled in detail, distinguishing across multiple vehicle types: - Conventional internal combustion engine vehicles (ICEVs, gasoline and diesel) - Hybrid electric vehicles (HEVs) - Plug-in hybrid electric vehicles (PHEVs) - Compressed natural gas (CNG) vehicles - Liquefied petroleum gas (LPG) vehicles - Battery electric vehicles (BEVs) - Hydrogen fuel cell vehicles (FCEVs) The model simulates the evolution of new vehicle sales using a multinomial logit framework, combined with cost-based comparisons to reflect consumer preferences. This allows technology adoption to respond endogenously to economic signals. For example, declining battery costs or increasing fossil fuel prices can lead to a higher share of electric vehicles in new registrations. Over time, the interaction between new sales and vehicle retirements determines the total fleet composition, from which the average efficiency and fuel mix are derived. A similar modelling approach is applied to other transport modes, including buses, freight vehicles, and rail (e.g. diesel versus electrified systems). Each mode is characterised by an evolving energy intensity, reflecting both technological improvements and shifts in the underlying technology mix. Using projected transport activity and mode-specific energy intensities, the module calculates total energy demand by fuel type and transport mode. Outputs include the consumption of major fuels such as gasoline, diesel, electricity, hydrogen, and other energy carriers used across the transport sector. These outputs are passed to other components of the modelling framework. Electricity and hydrogen demands are integrated into the Power Supply and Hydrogen modules respectively, while liquid fuel demand (e.g. gasoline, diesel, kerosene, and marine fuels) is transferred to the corresponding supply sector. In return, the Transport module receives updated fuel prices — such as electricity price, hydrogen costs, and retail fuel prices — which influence subsequent iterations of technology choice and transport demand. The module also incorporates a calibration mechanism. Regulatory measures such as CO₂ emission standards or electric vehicle mandates can be represented through parameters affecting technology uptake (e.g. maturity or adoption constraints), thereby influencing the composition of new vehicle sales. Finally, the Transport module includes vehicle stock dynamics, accounting for both standard lifetimes and premature scrapping. This enables the model to capture accelerated turnover in response to economic or policy drivers, particularly when certain technologies become less competitive. Within the integrated modelling framework, the Transport Demand module provides essential insights into the interaction between mobility trends, technological change, and energy demand. Its level of technological and modal detail enables the assessment of transition pathways, such as the impact of large-scale vehicle electrification on electricity demand and oil consumption, or the role of alternative fuels in reducing emissions from aviation and maritime transport. :::{seealso} As with industry, the transport module's electricity and hydrogen demands flow to the supply modules — see {ref}`power-supply` and {ref}`hydrogen-supply`. ::: (cdr)= ## Carbon Dioxide Removal (CDR) technologies The CO₂ module includes the calculation of carbon captured by the CCS and DAC technologies, as well as the curve of the sequestration costs according to the cumulative carbon captured as a result of the limitation in carbon storage. The rest of the module analyses the capacity expansion in CDR technologies. The following CDR technologies are added in OPEN-PROM: 1. Liquid solvent (high temperature) Direct-Air-Capture 2. Liquid solvent (high temperature) Direct-Air-Capture (Hydrogen fuelled) 3. Solid sorbent (low temperature) Direct-Air-Capture 4. Enhanced Weathering Input data for the technologies, such as capital, fixed and variable costs, were taken mainly from the UPTAKE project. The gross capital cost, fixed O&M cost, and variable cost are each affected by a learning curve (learning-by-doing) that scales them with cumulative installed capacity raised to the power `log(0.75)/log(2)`, i.e. a 25% cost reduction for every doubling of the installed capacity. Each cost is floored at a technology-specific minimum: the applied cost is the larger of the learning-curve value and that minimum (a smooth `max`), so costs decline with deployment but never fall below the parameterised floor. The expansion of these technologies is performed according to two basic criteria: - **Financial sustainability** is checked by calculating the ratio between the carbon taxes and the levelised carbon capture cost for each technology. Carbon taxes are supposed to increase as time passes, while at the same time the levelised cost decreases because of the learning curve. Moreover, this levelised cost is affected by the subsidy mechanism. This ratio affects the annual increase in the installed capacity, increasing the motivation for new investments where profitability is high. - **Regional emissions** create an extra motivation for CDR technology expansion. This adds regionality to the CDR technology expansion, ensuring that highly emissive regions have an extra reason to invest in CDR. This may eventually be linked with the subsidy mechanism, so as to express this extra motivation as a policy. :::{note} These drivers influence both the regional distribution of technology deployment and the definition of scenarios. On the one hand, regional differentiation is reflected in the expansion of technologies, as subsidies, costs, and residual emissions vary across regions. On the other hand, scenario design is shaped by the parameterisation of the subsidy mechanism, which represents policy choices specific to each scenario. :::