Giulio Breglia (p), Giulia Valeria Sonzogno

Vassilis Monastiriotis

The green transition is a defining challenge for economic and environmental policies, yet its spatial distribution remains highly uneven, raising concerns
about territorial equity. While sustainability policies aim to mitigate climate change and foster resilience, they interact with existing regional disparities, potentially reinforcing inequalities (Iammarino et al., 2019). The European Union (EU), through Next Generation EU (NGEU), integrates climate goals into post-pandemic recovery strategies, allocating 37% of the Recovery and Resilience Facility (RRF) to green projects (Commission, 2019). However, how these investments translate into territorial development remains largely unexplored. Using Italy’s National Recovery and Resilience Plan (NRRP) as a case study, we classify over 92,000 green projects through a GPT-supervised clustering
approach and analyze their socio-economic determinants using a multinomial logit model. Our findings reveal significant spatial disparities: metropolitan regions attract high-value investments, while peripheral and less-developed areas focus on low-value sustainability measures. These results underscore the need for place-based strategies to ensure a just transition. By integrating machine learning with spatial analysis, this study advances the understanding of sustainability policies and provides a methodological framework for assessing green transition initiatives.

Vassilis Monastiriotis (p)

Francesco Molica

Over the last 20 years a large literature has developed evaluating the effects of Cohesion Policy on regional growth and convergence. While the literature has told us a lot about whether and how Cohesion Funds work (Mohl and Hagen, 2010; Becker et al, 2012; Fratesi and Wishlade, 2017; Di Cataldo and Monastiriotis, 2018), attention on _how_ regions grow - with or without Cohesion Funds - has been at best limited. In this paper we pursue a comparative examination of the long-term regional growth processes found across the European economic space (i.e., examining the drivers of regional growth) and examine how these vary across groups of regions with different types (or ‘doses’) of 'treatment' in terms of Cohesion Policy. To do so, we borrow methodologically from the wage-decomposition techniques typically utilised in the applied labour economics literature. We specify an extended growth equation which we estimate separately for different groups of regions and then implement a detailed Blinder-Oaxaca decomposition in order to identify the contribution of two different components to each group’s growth performance: an ‘endowment’ component, which accounts for differences in performance due to differences in endowments (e.g., regional level of education or investment) and a ‘productivities’ component, which accounts for differences in performance that are due to the productivity of each of the different endowments considered. This analysis helps us unveil the fundamental differences in the growth process between Cohesion Policy’s beneficiary and non-beneficiary regions. Drawing on this, we engage in a critical discussion of the ‘suitability’ of EU Cohesion Policy to support balanced growth across the EU, within the context of the wider EU industrial policy objectives (digital and green transitions, economic sovereignty, strategic autonomy).

Francesco Molica (p), Anabela Santos

Giulio Breglia

In recent years, a new framing of innovation and industrial policies has emerged—often referred to as mission-led or transformative policies—which expands the traditional focus on economic and technological objectives to include societal (or sustainable development) goals, such as climate mitigation. Regional factors are critical for mission-led policies in two key ways: first, the exposure to societal challenges targeted by these policies varies significantly across regions; second, the effectiveness of these policies is highly influenced by specific spatial factors. This article explores these two intertwined and relatively understudied dimensions, with a particular focus on lagging regions. The first part unpacks the main factors that make these areas structurally more vulnerable to societal challenges and less capable of adopting—or fully benefiting from—transformative innovation and industrial policies designed to address such challenges. The second part assesses these dimensions empirically, focusing on climate change. Specifically, we develop a composite indicator that measures the vulnerability of European regions to the effects of climate change. We then assess the extent to which regional innovation and industrial policies and investments prioritise climate-related challenges, particularly in lagging regions. To this end, using text mining techniques, we analyse the priorities outlined in regional smart specialisation strategies and the climate-related projects funded by EU programmes at the regional level. The findings reveal the existence of a transformative innovation paradox: less developed regions, despite facing greater exposure to climate change, struggle to effectively develop policies and mobilize innovation to tackle it.