It is evident that the process of green transition reshapes national, regional, and local economic landscapes, and triggers change in ecosystems of various industries, thereby highlighting the urgent need to plan efficient and fair strategies to minimise the costs of job destruction and maximise the benefits of job creation in a socially fair way and a geographically equal way, in the wider patterns of reskilling. ISABEL's main objective is to provide a threefold solution to the aforementioned problem related to JCD with the use of new technologies such as AI and Large Language Models (LLM) that will 1) enrich our understanding of the socially and geographically uneven implications of this process across Europe, 2) broaden our knowledge on the factors underlying this process, and 3) highlight pathways of minimising the effects of job destruction and maximising the benefits of job creation, in a socially and spatially fair way. The latter include upskilling and reskilling of workers and the reallocation of labour, based on existing and forecasted skills shortages, using AI technologies to support these tasks. To achieve its main goal, ISABEL will examine the above elements at 3 different levels: i) the aggregate European level, among the EU, UK and Serbian Nomenclature of Territorial Units for Statistics 2 regions (NUTS2), using secondary data, ii) at 6 countries of focus -Denmark, Greece, Poland, UK, Serbia, Spain, collecting and analysing primary data, and iii) Living Labs focusing on 6 regions with diverse socio-economic features.