Man0EUvRE CS3 Dataset: Renewable Pulls and Industry Relocation

Final Industrial Energy Demand under Renewable Energy Endowment Shocks – Simulation Results from Case Study 3 (Man0EUvRE Project)

Description:

This dataset contains simulation results on sector- and country-level final industrial energy demand generated by the agent-based macroeconomic model developed in Case Study 3 (CS3) of the Man0EUvRE project (“Energy System Modelling for Transition to a net-Zero 2050 for EU via REPowerEU”, Grant Agreement No. 101069750, co-funded by the European Commission under the CETPartnership Joint Call 2022).

Scientific context

The transition to renewable energy reshapes industrial competitiveness because the distribution of renewable resources is geographically uneven. Regions endowed with abundant low-cost renewable electricity may develop new comparative advantages, potentially attracting industrial production – a mechanism referred to as the renewable pull effect (Samadi et al., 2023). CS3 investigates how such heterogeneous renewable energy endowments affect industrial relocation decisions and the resulting country-specific final energy demand across Europe.

The underlying model is a discrete-time, agent-based, stock-flow consistent macroeconomic simulation framework built with the open-source sfctools library (DLR). It represents 30 industrial sectors across 11 European countries in a multi-regional input–output structure calibrated to EXIOBASE 3.9.5. Firms are heterogeneous agents that compare unit production costs across countries and may relocate probabilistically (multinomial logit rule with home bias and congestion frictions) or – in an extension scenario – switch products within a capability-constrained product space. Energy endowment shocks are derived from the renewable export cost index of Kan et al. (2025) and applied as permanent proportional changes to country-level energy endowments at the mid-point of each simulation run (T = 340 periods, 20 Monte Carlo repetitions per scenario).

Dataset contents

The dataset consists of two files reporting Monte Carlo summary statistics of final industrial energy demand:

• CS3_IAMC_2022_means.xlsx – Monte Carlo means across 20 simulation runs
• CS3_IAMC_2022_medians.xlsx – Monte Carlo medians across 20 simulation runs

Both files follow the IAMC data format (long format: Model / Scenario / Region / Variable / Unit / 2022) and report final energy demand in EJ/yr for the post-shock equilibrium state. Variables include sector-level demand for 30 explicitly modelled industries (e.g. Final Energy|Industry|C_STEL for steel, Final Energy|Industry|C_CHEM for chemicals) as well as aggregate categories (Final Energy|Industry, Final Energy|Industry|Other, Final Energy|Industry|FossilFeed