Abstract
The transition towards climate-neutral industry is a challenge, particularly in heavy industries like steel and basic chemicals. Existing models for assessing industrial transformation lack spatial resolution and exogenous assumptions about process diffusion fail to capture individual investment decisions. Consequently, the spatial interplay between industry transformation, energy availability, infrastructure availability, and the dynamics of discrete investments is inadequately addressed.
Here, we present an agent-based approach to model energy-intensive industries with high spatial resolution. The model considers individual industrial sites to simulate discrete investment decisions. The investment decision is modelled as a discrete choice among alternative technologies with their total cost of ownership as the main decision criterion. Process costs depend on the scenario-specific framework, policy instruments and local infrastructures. By integrating the choice algorithm into a stock approach that tracks individual vintage, the age of production units and their reinvestment cycle are considered the main restrictions on the dynamics of the transition.
The results provide insights into the spatial and temporal dynamics of industry transition under varying process and policy assumptions. The presented model can be applied to all regions, industry sectors and processes.
We conduct an exemplary case study for a transformation pathway of the European primary steel production.