The goal of this project is to develop a new framework to characterize the distribution of transition risks in the energy sector. To achieve this, we develop a bottom-up model of energy firms and their investments in power plants. In this model, technology improves endogenously based on statistical learning curve models and is calibrated on a novel dataset of firm-specific energy assets, covering almost all power plants globally. We analyze the empirical investment dynamics in the energy sector to improve our understanding of the ability of firms to adapt to technological change. Building on the newly developed model, we will quantify transition risks at the firm level in the energy sector and evaluate the economic impact of various policy measures targeted at enhancing the green transition.

At the core of this project, we’re developing a comprehensive data-driven agent-based model (ABM) of the global energy sector. This model allows us to simulate the energy sector at the level of individual firms, bridging two disciplines by combining ABMs for the energy sector with economic models used for scenario development of the energy transition. We’re representing actual companies with their balance sheets and cash flows as agents in our model and numerically quantifying their probability of default. The granularity of the model enables us to account for a wide range of heterogeneity across companies, providing policy insights that were previously unattainable.

The project is funded by the Anniversary Fund of the OeNB (grant no. 18943).