A Framework for Assessing the Readiness and Vulnerability of the Romanian Territories Affected by the Just Transition Process

The paper consists of an exploratory study which seeks to build a multi-criteria index to assess the vulnerability and the readiness of regions affected by the energy transition, based on data collected from six Romanian affected regions (Dolj, Gorj, Hunedoara, Mureș, Galați and Prahova). Literature points out both the distributional and the procedural dimension of the just energy transition process, emphasizing that the current energy transition process is distinct from previous ones due to the complex actors constellation and to the policy planning behind it. The current research is a first attempt to use various socio-economic and demographic indicators towards developing composite indexes that allow for a better understanding of the just transition processes across Europe, testing enablers and disablers of a successful transition process. The exploratory study examines two premises: that coal regions are more vulnerable than emissionsintensive regions which are not relying on coal and that the readiness of regions to embark on a transition process is directly proportional to their average GDP/ capita. I use several indicators as proxies to assess the vulnerability level (unemployment, GDP/ capita, the average monthly salary, the technological intensity of the local economies, the share of the workforce employed in industry) and others to assess the readiness level (the share of employees in transition-affected large enterprises in the total share of employees, the level of interest and engagement of local stakeholders, EU funds absorption capacity at local level). Data analysis shows surprisingly high variance of the same indicator between affected regions and unexpected correlations, such as a negative correlation between regional wealth and local authorities’ capacity to attract EU funds. It also underlines the need for a larger number of cases and more indicators in the index, to make the results statistically significant and to compensate for the high variance among indicators.