Annex 4 analytical methods

model description

general description

acronym: FORECAST Industry

name: Forecasting Energy Consumption Analysis and Simulation Tool (model part: industry)

main purpose: FORECAST Industry is a bottom-up simulation model. It calculates scenarios of energy demand and GHG-emissions of the industry sector. It is mainly applied to scenarios addressing climate change and industry transformation.

homepage: https://www.forecast-model.eu/forecast-en/index.php

Developer and its nature

ownership: Third-party ownership (commercial companies, Member States, other organisations, …)

ownership additional info: Fraunhofer Institute for System- and Innovation Research (ISI), Germany.

is the model code open-source?: NO

Model structure and approach with any key assumptions, limitations and simplifications

details on model structure and approach

FORECAST Industry is based on European or national energy balances (e.g. Eurostat, AGEB (Germany)). In addition, it uses techno-economic data from literature and production statistics to define properties of around 80 industrial processes (e.g. energy intensity, deployed and potential future technologies). The combination of these techno-economic data and scenario-based assumptions on industrial activity (physical production of specific products) yield energy demands per product, process and subsector. This energy demand can be influenced by energy efficiency, fuel switch (driven by fuel prices or regulations), technology replacement (e.g. electric boilers replacing natural gas-fired ones) or complete process switch (e.g. hydrogen-based direct reduction instead of blast furnace operations) – all of which may be impacted by policies included in the scenarios.

In this calculation process, additional information are added to the statistical data with which the model started – thus, the process is called "disaggregation of energy balances". The disaggregated description of the industry sector is used to calculate possible futures in scenarios. In a final step, the model results are aggregated back towards the level of energy balances and calibrated to a given base year. The results thus represent a structurally compatible extension of the base statistic into the future along the scenarios defined in the project, with the main output of final energy demand, energy carrier shares and GHG-emissions by source (compatible with National Inventory Reports (NIR)).

model inputs

Energy balance (Eurostat, national balances)
Various techno-economic data on industrial processes (e.g. temperature profile of heat demand, energy intensity, investment and operation costs of technologies,…)
GHG-emissions (National Inventory Reports, common reporting tables)
Scenario definitions (including expected development of all input data)
Policy instrument description
Past and current activity of industrial processes (physical production)
Energy carrier prices
CO2-pricing
Gross domestic product

model outputs

Final energy demand by energy carrier, subsector, technology, temperature level, country
Greenhouse gas emissions (GHG) by subsector, energy carrier, country
Selected (not full) cost data (e.g. investment, energy, policy, CO2)

Intended field of application

policy role: FORECAST Industry may contribute to the assessment of potential impacts of new technologies on the industry sector and how policy instruments affect their performance. Examples include estimates of future hydrogen and electricity demand that result from different technology choices in the context of climate change mitigation.

policy areas

Climate action
Energy
Business and industry

Model transparency and quality assurance

Are uncertainties accounted for in your simulations?: NO - Uncertainties are addressed with scenario variations. Multiple scenarios allow to narrow the solution space of "possible scenarios" and what they mean for the model output.

Has the model undergone sensitivity analysis?: YES - Sensitivity analysis is addressed with scenario variations. Multiple scenarios allow to narrow the solution space of "possible scenarios" and what they mean for the model output.

Has the model been published in peer review articles?: YES

Has the model formally undergone scientific review by a panel of international experts?: NO

Has model validation been done? Have model predictions been confronted with observed data (ex-post)?: NO

To what extent do input data come from publicly available sources?: Based on both publicly available and restricted-access sources

Is the full model database as such available to external users?: NO

Have model results been presented in publicly available reports?: YES

Have output datasets been made publicly available?: YES

Is there any user friendly interface presenting model results that is accessible to the public?: YES

Has the model been documented in a publicly available dedicated report or a manual?: YES

Intellectual property rights

Licence type: Non-Free Software licence

application to the impact assessment

Please note that in the annex 4 of the impact assessment report, the general description of the model (available in MIDAS) has to be complemented with the specific information on how the model has been applied in the impact assessment.

See Better Regulation Toolbox, tool #11 Format of the impact assessment report).