# Breaking free from the regional carbon trap: analysing the persistence of CO 2 emissions in EU regions

Sébastien Bourdin, Arsène Perrot

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Sébastien Bourdin, Arsène Perrot. Breaking free from the regional carbon trap: analysing the persistence of CO 2 emissions in EU regions. Cambridge Journal of Regions, Economy and Society, 2025, pp.1-19. 10.1093/cjres/rsaf038. hal-05376844

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# Breaking free from the regional carbon trap: analysing the persistence of CO₂ emissions in EU regions

Sébastien Bourdin<sup>a,b</sup> and Arsène Perrot<sup>c</sup>

## Abstract

This study examines the persistence of CO₂ emissions across European regions through the analytical lenses of path dependence and socio-technical lock-ins. It introduces the concept of the “regional carbon trap” to account for enduring emission patterns. Drawing on data for EU NUTS-2 regions from 1990 to 2022, the analysis identifies key factors that either constrain or enable progress towards a low-carbon transition. The study proposes a novel typology distinguishing four regional trajectories: Virtuous Loop, Carbon-Intensive Trap, High-Emission Trap and Evolution Trap. The results underscore the pivotal influence of industrial specialisation, governance quality and economic diversification in shaping emission trajectories. Based on these findings, the paper formulates policy recommendations focused on targeted exnovation, economic diversification and technological leapfrogging. These recommendations stress the importance of adopting place-sensitive strategies to meet the European Union's decarbonisation objectives.

**Keywords:** regional path dependence, development trap, carbon emissions, transition, carbon lock-in, regional disparities

**JEL Classifications:** O18, O44, Q54, R11

## Introduction

Over the last decades, the growing importance of concepts such as path dependency and lock-ins has transformed our understanding of regional development dynamics (Goldstein et al., 2023; Martin, 2010; Martin and Sunley, 2010), especially in the context of the transition towards the low-carbon economy (Seto et al., 2016; Unruh, 2000, 2002; Unruh and Carrillo-Hermosilla, 2006). While climate change and recent energy crises have shed light on the necessity to rethink our development models, mechanisms of structural inertia keep slowing down the transformation efforts (Apajalahti and Kungl, 2022). The global poly-crisis — the COVID-19 pandemic, war in Ukraine, disruptions in global value and logistical chains, etc. — has increased the need to adapt faster. Still, these pressures often

clash with historical legacies and inherited socio-technical structures, creating tensions between the need to transform and inertial forces (Eitan and Hekkert, 2023).

The literature on path dependency, initiated by the seminal papers of David (1985, 1994) and Arthur et al. (1987), was later enriched by the integration of the concepts of lock-in in economic geography (Grabher, 1993; Hassink, 2010; Martin and Sunley, 2006, 2010). These studies helped reveal how self-reinforcing mechanisms, be they cognitive, functional or political, can maintain regions in unsustainable development trajectories. In this context, the concept of “carbon trap,” introduced by Unruh (2000), has become particularly relevant to explain the slowness of transitions towards a low-carbon economy, despite the climate emergency (Rosenbloom and Rinscheid, 2020; Seto et al., 2016).

<footnote><sup>a</sup>Métis Lab, EM Normandie Business School, 9 rue Claude Bloch, Caen 14000, France, sbourdin@em-normandie.fr</footnote>

<footnote><sup>b</sup>Centre for European Studies, Alexandru Ioan Cuza University, 19 Boulevard Carol I, Iași 700505, Romania, sbourdin@em-normandie.fr</footnote>

<footnote><sup>c</sup>Social Sciences, Gran Sasso Science Institute, Viale Luigi Rendina 26-28, L'Aquila 67100, Italy, arsene.perrot@gssi.it</footnote>
Although studies have shown path dependencies and lock-ins to be contextually created and thus geographically differentiated, few studies have explored how these dynamics play out at the regional level in Europe (Newey and Coenen, 2022). Moreover, even if literature on carbon lock-ins has emerged over the last few years (Goldstein et al., 2023; Seto et al., 2016), regional disparities in carbon emissions and the effects of industrial specialisation on carbon lock-ins persistence are still insufficiently studied. This article aims to fill this gap by studying the disparities in CO₂ emission pathways in European regions.

The current literature on sustainable transitions puts forward the importance of socio-technical and political lock-ins in slowing down the necessary changes to create a low-carbon economy (Simoens et al., 2022). Yet, two important gaps remain. First, a lack of empirical studies tackling how lock-in dynamics are influenced by specific regional characteristics. Most previous studies have focused on case studies, whether at national (Buschmann and Oels, 2019; Wang et al., 2020), sectorial (Janipour et al., 2020; Klitkou et al., 2015) or regional (Newey and Coenen, 2022; Rentier et al., 2019) level, leaving aside the study of interregional differences. Second, although the concept of carbon lock-in is backed up by empirical evidence, the mechanisms that allow some regions to “ unlock ” remain underexplored, most notably regarding the role of investment and innovation policies or structural changes in the industrial composition.

Consequently, our research question is the following: how do path dependencies and lock-in dynamics influence the regional disparities in CO₂ emissions in Europe? What are the factors that can allow these regions to escape from the high carbon intensity trajectories? By answering these questions, our study contributes theoretically, empirically and politically to the literature on path dependencies and socio-technical lock-ins, while contributing to shaping an answer to the challenges posed by regional disparities when it comes to reducing CO₂ emissions.

In this article, we provide three main contributions to filling the main gaps in the literature. First, we introduce the concept of “regional carbon trap,” which applies to regions that inherited economic structures, industrial specialisation and sociotechnical dynamics that prevent them from reducing their high levels of CO₂ emissions. Our definition draws upon existing theories of the carbon trap but rethinks this concept to reflect subnational development trajectories in industrialised economies. By focusing on regional disparities and path dependencies, we provide a new understanding of why some regions remain trapped in unsustainable carbon-intensive trajectories, thus hindering Europe’s wider transition towards a low-carbon economy. Second, our study provides a significant empirical contribution by relying on a set of regional data spanning the period 1990–2022 for a larger set of the European Union’s

NUTS-2 regions. Through econometric analysis and accounting for regional characteristics, we identify the main factors that influence the levels of CO₂ emissions at the regional level. Finally, our paper proposes political implications for European policymakers looking to speed up the transition towards a low-carbon economy. By identifying some key factors that maintain places in regional carbon traps (i.e. carbon-intensive lock-in trajectories), we suggest some potential avenues for place-sensitive policies (Iammarino et al., 2019). Moreover, our theoretical framework of the “regional carbon trap” can help in shaping adapted regional strategies, in line with the EU decarbonisation aims, and notably the ambition to reduce greenhouse gas emissions of at least 55% by 2030 in comparison with 1990 levels, and pointing towards carbon neutrality by 2050, as defined by the European Green Deal and the “Fit for 22” strategy.

## Literature review

### Path dependence and lock-ins in economic geography

### Core concepts and regional implications

The concept of path dependence was introduced in the 1980s by David (1985) and Arthur et al. (1987) to explain how past technical and economic choices have a lasting influence on future development. For David (1985), network effects and the costs of change favour the persistence of the technologies adopted, even when more efficient alternatives become available. As for Arthur et al. (1987), he introduces the idea of increasing returns, showing that initial choices can lock a region or a sector into a specific trajectory, making subsequent changes difficult.

These concepts have later been extended to economic geography by authors such as Grabher (1993) or Martin and Sunley (2006, 2010), who explored how path dependency applies to regional contexts. Lock-in refers to a situation where self-reinforcing mechanisms lock a region into an unsustainable development trajectory. Grabher (1993) distinguishes three main forms of lock-in that explain the inertia of regional trajectories. First, cognitive lock-ins arise when stakeholders share homogenous thought frameworks that shape their decisions, which has the effect of limiting innovation and the adoption of new technologies. This lock-in is reinforced by collective learning and organisational routines, creating resistance to change. Second, functional lock-ins result from economic interdependencies between companies and sectors, generating relationships of mutual dependence that inhibit structural change. This type of lock-in is particularly observable in regions that are highly specialised in traditional industries, where supply chains and infrastructures are adapted to specific technologies. Finally, political lock-ins are linked to the inertia of local institutions, which are of-
ten influenced by entrenched economic interests. Policies to support polluting industries, motivated by local political pressure, contribute to this institutional inertia. Several authors have shown how these lock-in phenomena limit the capacity of these regions to adapt and transform (e.g. Antonietti and Boschma, 2021; Matera and Sokołowicz, 2023).

## From carbon lock-in to regional carbon traps

The concept of carbon lock-in, introduced by Unruh (2000), provides a useful theoretical framework to understand why some regions of the world are failing to reduce their CO₂ emissions despite ambitious energy transition policies. According to Unruh, carbon lock-in is based on the idea that socio-technical systems are deeply rooted in dependence on fossil fuels. These systems incorporate complex networks of actors, infrastructures designed for fossil fuel technologies and institutional norms that favour the maintenance of the status quo. This structural inertia, fuelled by self-reinforcing mechanisms, delays the adoption of renewable energies and slows down the transition to a low-carbon economy.

Yet while the original formulation of carbon lock-in offers valuable insights into systemic inertia, its predominant focus on national or sectoral scales overlooks the spatial heterogeneity of transition dynamics. In particular, it pays limited attention to how structural constraints materialise differently across regions and how subnational territories — embedded in distinct economic, institutional and infrastructural configurations — may exhibit highly variable capacities for decarbonisation (Vagnini et al., 2025).

In response, this study advances a shift from a generalised reading of lock-in to a territorially grounded conceptualisation: the “regional carbon trap.” This formulation draws on established insights from economic geography and transition theory, which demonstrate that structural constraints to decarbonisation are not uniformly distributed across space but reflect region-specific institutional, technological and economic legacies (Boschma, 2015; Newey and Coenen, 2022). Regional trajectories are shaped by the long-term accumulation of sectoral specialisation, infrastructure configurations and governance arrangements that restrict the strategic choices available to territorial actors.

The regional scale constitutes a relevant and analytically productive level of inquiry for several reasons. First, across Europe, the decarbonisation agenda has increasingly taken the form of territorially tailored strategies. Numerous regions have elaborated explicit ecological transition plans — formally integrated into regional development frameworks — which aim to align climate mitigation objectives with place-based economic renewal (Haughton and Counsell, 2004; Gianelle et al., 2020). These plans often operate through Smart Specialisation Strategies, which

seek to mobilise regional assets and institutional capacities in support of green innovation, while acknowledging asymmetries in structural readiness and industrial fabric (Foray et al., 2015). Second, a growing body of empirical studies has demonstrated that critical variables, such as government effectiveness, labour productivity, demographic structure and environmental performance, vary markedly across regions within countries (Charron et al., 2015; Rodríguez-Pose and Ketterer, 2020). These spatial differentials shape the capacity of regions to design and implement transformative policies and help explain why comparable national frameworks yield divergent climate outcomes at the subnational level.

Against this backdrop, the regional carbon trap concept offers an interpretative lens that captures the material and institutional constraints embedded within specific territorial contexts. It provides a means to theorise why certain regions remain structurally disadvantaged in the transition to climate neutrality, despite broader policy convergence at the national and European levels.

From this perspective, carbon lock-in is understood less as a function of cognitive or behavioural rigidities than as the territorial sedimentation of sunk costs, technological dependencies and administrative constraints. As argued by Sutton (1991), the economic logic of sunk and switching costs implies that actors facing substantial past investments in carbon-intensive assets will have reduced incentives to reallocate resources, even when more sustainable alternatives become available. Similarly, Janipour et al. (2020) show that in energy-intensive sectors such as the Dutch chemical industry, persistent emissions stem not from inertia in awareness, but from a complex interplay of long-lived infrastructure, production networks and policy frameworks that stabilise high-carbon modes of operation.

The regional carbon trap concept aims to synthesise these multiple dimensions by situating them within the context of place-specific trajectories. It draws attention to how material infrastructures, institutional path dependencies and economic structures combine to limit the transformative capacity of territories. Within this analytical frame, the term “trap” refers to a limitation of transformative capacity rather than a failure of perception. It brings to the forefront the uneven distribution of costs and the political resistance that often accompanies efforts to dismanage fossil-fuel-dependent systems (Bourdin, 2024; Delcayre and Bourdin, 2025). Furthermore, it captures the diversity of regional contexts in terms of institutional robustness and economic flexibility. In the European setting, the concept of the regional carbon trap offers an interpretative tool to examine why some territories face enduring obstacles in aligning with climate objectives such as the 55% emissions reduction target by 2030 and the broader commitment to carbon neutrality by 2050 articulated in the European Green Deal.
## Regional carbon traps: determinants and strategies to break free

The persistence of high-carbon trajectories stems from the accumulation of sunk costs and the structural disincentives associated with switching to alternative technologies (Sutton, 1991). These switching costs — rooted in pre-existing capital investment, sectoral specialisation and institutional entrenchment — create economic and political barriers that limit the adoption of more sustainable options.

## Structural barriers and regional dynamics

Macroeconomic analysis has shown that countries that are highly specialised in carbon-intensive industries, such as steel, chemicals and cement, are particularly prone to being trapped in high-carbon trajectories (Bataille et al., 2016; Buschmann and Oels, 2019; Fischer and Newell, 2008). These sectors, characterised by heavy infrastructure and long-lasting capital, generate high fixed costs, making their conversion both economically and politically difficult.

In addition, industrial specialisation in carbon-intensive sectors is reinforced by economic and technological barriers. In particular, the lack of renewal of industrial capital keeps obsolete infrastructures in place, resulting in little or no investment in cleaner technologies and maintenance of obsolete infrastructures (Turnheim, 2022). This inertia is often amplified by the persistence of public subsidies to fossil fuel industries, which encourages dependence on polluting energies and delays the transition (Apajalhti and Kungl, 2022). A priori, the regional carbon trap will therefore tend to manifest itself particularly in regions where there is a low rate of capital renewal.

The quality of government represents a structural determinant of regional decarbonisation capacity and must be approached as more than an administrative background variable. It encompasses a multidimensional set of attributes — including rule of law, institutional accountability, bureaucratic competence and the capacity to implement and enforce policy — which together shape the ability of regions to formulate, coordinate and deliver coherent transition strategies (Charron et al., 2015; Rodríguez-Pose, 2013). Recent contributions in regional development studies have highlighted that higher-quality government institutions are associated with more effective use of public resources, stronger innovation systems and increased policy legitimacy, all of which contribute to more robust responses to long-term structural challenges such as climate change (Dijkstra et al., 2019; Grillitsch et al., 2019. Conversely, regions marked by low institutional quality — where clientelism, fragmented authority or regulatory capture prevail — tend to experience slower transitions, limited stakeholder engagement and policy instability. These weaknesses inhibit the formation of durable low-carbon trajectories, as the credibility and continuity of decarboni-

sation agendas are compromised (Tomaney and Pike, 2020; Vagnini et al., 2025; Delcayre and Bourdin, 2025). Furthermore, institutional quality does not operate in isolation but interacts with socio-economic and structural conditions to shape differentiated regional capacities for transition. For instance, Grillitsch et al. (2019) emphasise the role of institutional thickness in fostering innovation-led renewal, particularly where coordinated efforts are required across administrative levels and sectors. In this light, the capacity to govern transition processes is not uniform, and understanding regional variation in government quality is essential to identifying why certain territories persist in high-emission equilibriums despite formal commitments to decarbonisation.

Moreover, regional affluence — measured through GDP per capita — exerts ambivalent effects on transition pathways. Higher income levels increase the availability of financial resources for investment in innovation, infrastructure and workforce development. However, affluent regions often display elevated levels of consumption-related emissions, especially in domains such as mobility, housing and digital infrastructure (Kerkhof et al., 2009; Wang and Li, 2021). This duality positions economic prosperity as both a source of capacity and a structural driver of carbon-intensive lifestyles. Carbon traps, therefore, can result from production systems but also from patterns of demand embedded in regional consumption models.

Finally, a high level of education is often associated with a greater capacity of regions to adapt, thanks to a more skilled workforce and higher levels of innovation (Rodríguez-Pose and Ketterer, 2020; Wu and Liu, 2021). In the same line, investments in research and development (R&D) play an essential role in the low-carbon transition. Regions that devote a significant proportion of their GDP to R&D tend to be better equipped to innovate and develop green technologies (Truffer and Coenen, 2012), with a consequent reduction in their carbon intensity (Geels and Schot, 2007; Luo and Zhang, 2022).

## Strategies to break free from regional carbon traps: exnovation, diversification and leapfrogging

Several studies have shown that path dependency and lock-in are contextual and geographically differentiated, which means that solutions to break these locks must be tailored to the specific regional shocks (Boschma, 2015; Newey and Coenen, 2022). The literature distinguishes several strategies for getting out of high-carbon trajectories and overcoming regional carbon traps.

Among the identified strategies, the concept of exnovation occupies a central place. Introduced by Turnheim (2022), it refers to a proactive approach aimed at dismantling obsolete infrastructures and phasing out polluting technologies that are holding back the ecological transi-
tion. This strategy is particularly relevant in regions where the rate of renewal of industrial capital is low. The literature also highlights the greater effectiveness of exnovation when it is coupled with significant investment in R&D. These investments stimulate technological innovation and accelerate the adoption of green solutions (David, 2017). This gives regions the means to redirect their development trajectories towards more sustainable models.

Economic diversification, often associated with the concept of path break-out, is a key strategy for moving away from high-carbon trajectories and overcoming regional carbon traps (Apajalahti and Kungl, 2022). This approach is based on the idea that regions that diversify their industrial base are better equipped to break free from historical dependence on polluting industries and adopt low-carbon technologies. Unlike trajectories dominated by mono-specialisation in specific sectors, where lock-in effects are reinforced by rigid value chains and infrastructures, regions with a diverse industrial base show a greater capacity for adaptation and resilience in the face of the structural changes required for the ecological transition (Boschma and Frenken, 2018).

Success stories from Europe, particularly East Germany, illustrate how targeted diversification policies have helped to break down the industrial barriers inherited from the Soviet era. In this region, investments in emerging sectors, coupled with public policies to support innovation, have transformed an industrial landscape heavily dependent on fossil fuels into a more balanced ecosystem focused on clean technologies (Trippl et al., 2020).

In a broader context, the literature emphasises that economic diversification relies on proactive development strategies, such as strengthening local R&D capabilities, improving the quality of infrastructure and creating ecosystems conducive to the emergence of new industries (Grashof and Basilico, 2024; Isaksen et al., 2022). Moreover, regions that diversify successfully tend to mobilise strong local institutions, which play a key role in coordinating public and private players to maximise the impact of transition policies (Rodríguez-Pose and Wilkie, 2017).

Finally, leapfrogging, as defined by Unruh and Carrillo-Hermosilla (2006), offers a particularly interesting perspective for lagging-behind regions. This concept is based on the idea that these regions can bypass traditional industrial development trajectories and adopt renewable technologies and sustainable energy systems directly. In other words, these regions have a unique opportunity to avoid costly and polluting intermediate stages and move directly into a low-carbon economy. However, leapfrogging requires a solid quality of government and coordinated investment at regional, national and European level. Furthermore, the literature highlights the importance of technology transfer policies in the success of leapfrogging initiatives, as these regions do not *a priori* have the necessary initial resources to develop their own technologies. When properly targeted,

these transfers help to overcome structural obstacles by providing ready-to-use solutions adapted to local contexts (Cooke, 2013). They also strengthen local innovation capacity, a key element in maintaining a sustainable development trajectory over the long term (Geels and Schot, 2007).

## Methodology

### Data

To carry out the empirical analysis, we retrieve data from different sources relating to regional environmental, economic and structural characteristics. This data, which covers the period from 1990 to 2020 at the NUTS2 regional level, allows us to highlight how CO₂ emissions pathways are influenced by place-specific characteristics in EU regions.

### CO₂ emissions

First, we retrieve data regarding the level of emissions at grid level from the EDGAR database (Pagani et al., 2023), as well as their equivalent per capita, that we reconcile with the NUTS regions. We use this variable as the main variable of interest of our study. We rely on it to construct our identification of regions into the Carbon Trap Status before integrating it as the dependent variable in our econometric models.

### Industrial composition

From the Joint Research Center's ARDECO database and Eurostat, we retrieve the Sectoral Distribution of Employment at the 2-digit level of the NACE classification. This allows us to construct a variable that captures the relative industrial specialisation of the region. Consequently, we use the Location Quotient (LQ) of manufacturing activities in the region, i.e. the relative specialisation of the region into manufacturing industries. Specifically, we consider as manufacturing sectors for this measure the 1-digit sectors B to E of the NACE classification (namely, Mining and Quarrying; Manufacturing; Electricity, gas, steam and air conditioning supply; Water supply, sewerage, waste management and remediation activities). Our LQ is constructed using the following definition:

$$LQ_{rt} = \frac{e_{rst}/e_{rt}}{e_{st}/e_{t}}, \qquad (1)$$

where $e_{rst}$ is the total employment in manufacturing sectors in region r in year t; $e_{rt}$ is the total employment in the region r in year t; $e_{st}$ is the total employment in manufacturing sectors across the sample in year t; $e_t$ is the total employment across sectors in the sample in year t.

We use this measure for two main reasons. First, to test the assumption that regions with a more important presence of manufacturing are likely to show higher CO₂ emission levels. Second, we use this relative measure to understand if regions with different emissions trajectories
and carbon intensity show different patterns related to the presence of manufacturing activities. In other words, if virtuous regions host fewer polluting industries or are simply less industrialised.

Similarly, we construct and consider a LQ of service activities, considering a subset of service sectors with intensive use of Information and Communication Technologies (ICT)¹. We do so to capture the carbon-intensive potential of these sectors relying on ICT, which can be correlated with higher CO₂ emissions levels. Yet, some limitations to using this data exist. In particular, the NACE disaggregation does not allow us to disentangle between Tourism, Retail and Transportation activities. Consequently, we decide to leave those sectors out of the analysis. Still, the addition of this indicator allows us to understand whether regional disparities exist in the emission profile of services with the European Union.

## Manufacturing capital renewal

To help us decipher the relationship between manufacturing activities and CO₂ emissions at the regional level, we integrate into the analysis a measure of capital renewal in manufacturing activities. This indicator is constructed with data retrieved from ARDECO regarding the formation, consumption and stocks of capital at the NUTS2 regional level. It is defined as in Equation (2).

$$K \text{ Renewal}_{rt} = \left( \frac{K \text{ Formation}_{rt} - K \text{ Consumption}_{rt}}{K \text{ Stock}_{rt}} \right) * 100, (2)$$

where K Formation$_{rt}$ is the Gross Fixed Capital Formation for manufacturing sectors in region r in year t; K Consumption$_{rt}$ is the Gross Fixed Capital Consumption for manufacturing sectors in region r in year t; K Stock$_{rt}$ is the Capital Stock in region r in year t. All values are at constant prices in Million Euros 2015. With this variable, we measure the yearly rate of renewal of fixed capital in manufacturing sectors (B to E of the NACE classification) that we weight by the total stock of capital in the region. In the absence of data on the sectoral stocks, this regional weight allows us to normalise the values to the economic size of the regions and make this measure comparable throughout the EU.

We incorporate this variable into the analysis to test the assumption that older infrastructures and productive capital might be conducive to more emissions, while a higher renewal of fixed capital might indicate the opposite trend. Moreover, we aim to assess the possibility that some regions could have access to newer and greener technologies than others, which would be indicated by a different relationship between capital renewal and CO₂ emissions in those regions.

## Other variables

Finally, we integrate a last set of variables intended to account for the differentiated structural attributes of regions

and their influence on emission levels. First, GDP per capita is mobilised to examine the economic standing of regions. This indicator serves a dual analytical purpose. On the one hand, it reflects the financial resources potentially available to support decarbonisation investments. On the other hand, it also correlates with higher levels of consumption and lifestyle-related emissions, particularly in the domains of mobility, housing and digital infrastructures. As such, GDP per capita captures both enabling and constraining effects in the transition process and must be interpreted as an ambivalent factor. Second, we include the growth rate of GDP in order to assess whether regions are undergoing processes of economic expansion and whether these dynamics exhibit distinct relationships with emission trajectories across typologies. Third, we introduce labour productivity, measured in nominal terms per employee, as a proxy for the structural composition of regional economies. This variable allows us to distinguish between regions dominated by traditional industrial activities and those characterised by high value-added and knowledge-intensive sectors, which tend to exhibit different emission intensities and innovation capacities.

Second, we aim to understand the relationship between regions' demographic characteristics and emission levels. Thus, we collect information on the population to construct two different variables. First, we compute the population year-to-year growth rate, to capture the direct effect of an increase in population on energy consumption and needs, and other polluting activities such as transportation. Second, we integrate the population density in the region into the analysis. Although it presents a lot of heterogeneity within NUTS-2 regions, it allows us to understand how a higher settlement density within a region can be related to more emissions, through the aforementioned channels (energetic needs, higher traffic, etc) or if, on the contrary, they might be related to more public policies regarding transportation and reduced needs for private vehicles.

From Eurostat, we retrieve information on regional spending in R&D as a per cent of regional GDP. This allows us to assess the role of investment in R&D, which is shown to have a role in the energetic transition, by facilitating technological innovation to reduce CO₂ emissions. We also use the level of education in the region, particularly the share of the regional population with tertiary education (university degree). We integrate in the analysis the measure of quality of government from Charron et al. (2015, 2019, 2022, 2024). This measure, available for most European Union regions, helps us understand the role of regional governance and institutions in developing efficient transition strategies, or, conversely, in hindering the transition potential in locked-in regions. Finally, to assess how regionally differentiated patterns of consumption are related to different levels of CO₂ emissions, we integrate a measure of Domestic Material Consumption at NUTS-2 level
<figure><img src="image_2.png" /><figcaption>**Figure 1.** Typology of the regional carbon trap.</figcaption></figure>

(Bianchi et al., 2020), measured as the number of tons of materials consumed per inhabitant in the region that year.

## Stylised facts: definition and descriptive analysis of the regional carbon trap status

Analysing European regions' carbon trajectories requires a more fine-grained understanding of their pathways. To do that, we put together a framework that identifies the trajectories that regions have known over the last thirty years. This allows us to identify the underlying characteristics explaining the presence of regions in different groups. It is first important to understand the general trends of emissions in the EU. As shown in Figure 2, over the period 2000–2022, the average regional emission level has decreased from 11.4 tons of CO₂eq per capita to 8.5 tons of CO₂eq per capita — a significant number yet insufficient to reach the carbon neutrality objective set by the EU. However, this average number can hide important heterogeneity between different groups of regions.

Consequently, we aim to identify the different paths that European regions have known over the period of analysis. To do that, we classify regions into different categories of trajectories, based upon their CO₂ emission profile. We follow the literature on regional development traps (Diemer et al., 2022; Iammarino et al., 2020) and adapt Balland and Boschma's (2024) measure into a naïve framework to represent and capture the type of "Regional Carbon Emitters" that NUTS-2 regions fit into. Figure 1 represents graphically the typology that we use to classify regions. On the one side, we consider the level of emissions of the regions, considering the total carbon emissions from region r over the period. On the other side, we consider the evolution of emission levels in region r between 1990 and 2022. This measure uses emissions per capita, i.e. a relative measure, to allow for comparison between regions. To determine the quadrant in which each region falls, we use the ranking of each of these two measures relative to all the considered

regions in the EU. This allows us to have a relative comparison between all regions, where regions fall into low or high categories of both considered parameters depending on their value relative to the median value in the EU.

From that classification, we identify four types of regional carbon emitter profiles. First, regions locked in a "Carbon-Intensive Trap" (top-right quadrant) show high levels of carbon emissions and above-average rises in carbon emissions. From this classification, these regions are the ones that are or are most likely to be locked into a carbon trap. Second, regions present in a "High-Emission trap" (top-left quadrant). Those are the regions where the level of emission is historically high, but that manage to reduce or show below average rises in emissions. These regions show positive signs of transition. Third, regions in an "Evolution Trap" (bottom-right quadrant), that are lower emitting regions that have, however, known an above-average rise in carbon emissions. Those are regions that are at risk of future lock-ins if efficient measures are not taken. Finally, regions that are in a "Virtuous Loop" (bottom-left quadrant) are the regions where emissions are relatively low and either diminishing or rising slower than in other regions. These regions are the ones where we posit that the transition is engaged, managed and realised better than in the others.

From Figure 2, the heterogeneity that we conceptualised appears clearly. The within-group averages are quite far from the EU average; at the same time, the evolution of the four groups differs. First, High-Emission Trap regions, although the highest emitters at the beginning of the period, have significantly reduced their emission level by 2022, from 17 tons per capita to less than 11. Similarly, the Virtuous Loop regions have decreased their emission levels over the last 20 years. On the contrary, although limited, emissions of Evolution Trap regions have been stable over the period. More strikingly, despite their high initial emissions levels, regions that we identify as being locked in a regional
<figure><img src="image_3.png" /><figcaption>**Figure 2.** Evolution of emissions per capita of European regions according to their regional carbon trap status (2000–2022).</figcaption></figure>

carbon trap have barely reduced their emission levels: from 14.3 to 12.8 tons per capita. Figure 3 maps our classification, showing that regions classified as locked-in are more present in Southern and Eastern Europe (Spain, Italy, Poland), although we highlight the presence of trapped regions in Germany and France. On the other hand, regions identified as being in a Virtuous Loop are more present in France, Germany, as well as the Netherlands and parts of Eastern Europe. Table A1 in online Appendix 1 presents descriptive statistics of the different groups of regions that we identify. This data confirms the heterogeneity between our different groups and highlight their different profiles. In particular, the most virtuous regions are among the most economically dynamic ones, while the ones that are identified as being locked-in have lacks in regional development. At the same time, population dynamics seem to be quite similar between all groups, and high population density is not related to being identified as a more polluting region. It is also interesting to note that the rate of capital renewal is on average rather similar across all types of regions.

## Empirical specifications

In the next section, we provide a quantitative analysis of the relation between the set of variables presented previously and the level of emissions per capita in European regions. Consequently, we rely on a set panel linear model

estimated through Ordinary Least Squares (OLS), both on the full and different sub-samples. The most complete model that we analyse is described as follows:

$$Y_{rt} = \alpha + \beta (\delta_{rt}) * \text{Trap Status}_r + \tau_t + \omega_r + \varepsilon_{rt}, \quad (3)$$

where $Y_{rt}$ is the level of CO₂ emissions per capita for region r in year t. We relate this dependant variable to a set of regional characteristics $\delta_{rt}$ presented in previous sections, which include in its basic specification GDP per capita, labour productivity, the manufacturing LQ, population density, population growth, GDP growth and manufacturing capital renewal. Trap Status_r indicates the classification of the region in our typology. $\tau_t$ and $\omega_r$ are, respectively, year and regional fixed effects, used to capture time-constant unobserved heterogeneity between NUTS-2 regions and time-varying unobserved heterogeneity between years across all NUTS-2 regions; $\varepsilon_{rt}$ is the error term.

We then estimate this model on sub-samples determined by the regional carbon trap status to understand the different group dynamics and effects of the regional characteristics we aim to study. Finally, we use interaction terms as described in Equation (3), which allow us to assess if the between-group differences in the relation are statistically significant. To accommodate the distribution of the values and the presence of zeros in some observations, we transform all variables with the inverse hyper-
<figure><img src="image_4.png" /><figcaption>**Figure 3.** Regional carbon trap status, based on emissions per capita.</figcaption></figure>

bolic sine function. This allows us to keep the complete sample throughout specifications and read elasticities as if using a logarithmic transformation (Bellemare and Wichman, 2020).

## Results

Regression results in Tables 1 and 2 show significant relations between emissions per capita in European regions and the socio-economic and structural variables we consider. In this part, we analyse how these characteristics act differently in regions depending on their regional carbon trap status. To do that, we compare the average results in all European regions (Table 1) with the within-group dynamics using a sub-sampling method (Table 2), which we confirm statistically through interaction terms (Table A2). In these specifications, the comparison group is

always the virtuous regions, to understand how the other regions are placed in comparison to them. This method allows us to hint towards differentiated policies and strategies that could be needed for them to “unlock” from “lock-ins.”

## Analysis of the regional economic determinants of emissions per capita

First, the coefficient associated with GDP per capita remains positive and statistically significant across all model specifications (ranging from 0.448 to 0.707), thereby corroborating the hypothesis of a direct association between the level of economic development and regional CO₂ emissions. This outcome reflects the fact that affluent regions tend to exhibit more energy-intensive consumption profiles, frequently underpinned by lifestyles and infrastruc-
<table><caption>**Table 1.** Relation between CO₂ emissions per capita and our set of variables.</caption><thead><tr><th></th><th colspan="5">Dependent variable: CO₂ emissions per capita</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>GDPc</td><td>0.481***<br>(0.036)</td><td>0.448***<br>(0.038)</td><td>0.490***<br>(0.051)</td><td>0.707***<br>(0.061)</td><td>0.405***<br>(0.092)</td></tr><tr><td>Labour</td><td>-0.300***</td><td>-0.284***</td><td>-0.322***</td><td>-0.371***</td><td>-0.333***</td></tr><tr><td>Productivity</td><td>(0.038)</td><td>(0.039)</td><td>(0.054)</td><td>(0.066)</td><td>(0.082)</td></tr><tr><td>LQ</td><td>0.495***</td><td>0.453***</td><td>0.496***</td><td>0.223***</td><td>0.224***</td></tr><tr><td>Manufacturing</td><td>(0.034)</td><td>(0.035)</td><td>(0.050)</td><td>(0.063)</td><td>(0.077)</td></tr><tr><td>Capital</td><td>0.005</td><td>0.004</td><td>0.006</td><td>0.002</td><td>0.002</td></tr><tr><td>Renewal</td><td>(0.003)</td><td>(0.003)</td><td>(0.004)</td><td>(0.005)</td><td>(0.005)</td></tr><tr><td>Population</td><td>-0.262***</td><td>-0.260***</td><td>-0.186***</td><td>-0.574***</td><td>-0.793***</td></tr><tr><td>Density</td><td>(0.037)</td><td>(0.043)</td><td>(0.059)</td><td>(0.085)</td><td>(0.130)</td></tr><tr><td>Population</td><td>-0.010**</td><td>0.001</td><td>0.006</td><td>0.004</td><td>-0.015**</td></tr><tr><td>Growth</td><td>(0.004)</td><td>(0.004)</td><td>(0.006)</td><td>(0.006)</td><td>(0.007)</td></tr><tr><td>GDP</td><td>0.001</td><td>0.002</td><td>0.002</td><td>-0.001</td><td>0.0002</td></tr><tr><td>Growth</td><td>(0.001)</td><td>(0.001)</td><td>(0.002)</td><td>(0.002)</td><td>(0.002)</td></tr><tr><td>LQ</td><td>-0.387***</td><td>-0.296***</td><td>-0.330***</td><td>-0.200*</td><td>-0.112</td></tr><tr><td>Services</td><td>(0.060)</td><td>(0.066)</td><td>(0.094)</td><td>(0.108)</td><td>(0.132)</td></tr><tr><td>Education</td><td></td><td>-0.060***<br>(0.015)</td><td>-0.180***<br>(0.021)</td><td>0.066**<br>(0.030)</td><td>0.013<br>(0.033)</td></tr><tr><td>R&D</td><td></td><td></td><td>0.089***<br>(0.016)</td><td>0.034*<br>(0.019)</td><td>0.032<br>(0.022)</td></tr><tr><td>Government<br>Quality</td><td></td><td></td><td></td><td>-0.008<br>(0.011)</td><td>-0.0004<br>(0.013)</td></tr><tr><td>Domestic<br>Consumption</td><td></td><td></td><td></td><td></td><td>0.104***<br>(0.025)</td></tr><tr><td>Observations</td><td>5443</td><td>5084</td><td>3065</td><td>1791</td><td>1015</td></tr><tr><td>R²</td><td>0.218</td><td>0.196</td><td>0.228</td><td>0.303</td><td>0.267</td></tr><tr><td>F Statistic</td><td>180.151***</td><td>130.257***</td><td>82.779***</td><td>62.950***</td><td>26.032***</td></tr></tbody></table>

Note: *p < 0.1; **p < 0.05; ***p < 0.01.

Models' fixed effects are two-way (NUTS2-year).

tures that maintain a relatively high carbon footprint. The result also suggests that wealth, while increasing financial capacity for decarbonisation, remains systematically correlated with elevated emission levels when no structural shifts in consumption or production patterns are observed. This result is in line with lock-in effects, where these regions that massively invested in fossil fuel infrastructures can have more trouble reducing their emission levels despite their resources. The direction of the relationship is the same for GDP growth, although smaller in magnitude, and is still associated with emissions growth. Even though some regions might have managed to partially decouple economic growth and emission levels, this effect seems limited. The relationship between labour productivity and emissions is significant and negative (-0.284 to -0.371), which indicates that the most productive regions in Europe tend to emit less CO₂. We explain this result through two mechanisms: first, through a mechanical explanation, as the most productive sectors in the Euro-

pean industrial structure are knowledge intensive services, which are less carbon-intensive than others. Still, this first mechanism is alleviated by the presence in the model of a variable that captures the relative specialisation in both manufacturing and service industries. In that sense, the effect of labour productivity is in part cleared of this service effect. This is why, on the other side, this negative coefficient could suggest that a more efficient — and thus productive — industrial and manufacturing structure is an efficient strategy to reduce emissions in carbon-intensive regions.

This is also related to the coefficient observed for the relative industrial specialisation in manufacturing, which is positive and significant throughout our specification (0.223 to 0.496). This result confirms that manufacturing and energy-producing regions tend to exhibit higher emissions per capita. It supports our hypothesis that industrial specialisation plays a central role in the emergence of regional carbon traps. These regions remain more frequently bound
<table><caption>**Table 2.** Relation between CO₂ emissions per capita and our set of variables in different sub-samples.</caption><thead><tr><th rowspan="2"></th><th colspan="5">Dependent variable: CO₂ emissions per capita</th></tr><tr><th>Full sample<br>(1)</th><th>VL<br>(2)</th><th>CI<br>(3)</th><th>ET<br>(4)</th><th>HE<br>(5)</th></tr></thead><tbody><tr><td>GDPc</td><td>0.481***<br>(0.036)</td><td>0.516***<br>(0.081)</td><td>0.932***<br>(0.071)</td><td>0.430***<br>(0.056)</td><td>0.631***<br>(0.069)</td></tr><tr><td>Labour<br>Productivity</td><td>-0.300***<br>(0.038)</td><td>-0.362***<br>(0.084)</td><td>-0.739***<br>(0.072)</td><td>-0.136**<br>(0.059)</td><td>-0.471***<br>(0.073)</td></tr><tr><td>LQ<br>Manufacturing</td><td>0.495***<br>(0.034)</td><td>0.420***<br>(0.074)</td><td>0.173***<br>(0.064)</td><td>0.433***<br>(0.063)</td><td>0.312***<br>(0.057)</td></tr><tr><td>Capital<br>Renewal</td><td>0.005<br>(0.003)</td><td>-0.020***<br>(0.007)</td><td>0.007<br>(0.005)</td><td>0.025***<br>(0.007)</td><td>-0.019***<br>(0.006)</td></tr><tr><td>Population<br>Density</td><td>-0.262***<br>(0.037)</td><td>-1.225***<br>(0.106)</td><td>-0.666***<br>(0.082)</td><td>-0.160***<br>(0.054)</td><td>0.066<br>(0.070)</td></tr><tr><td>Population<br>Growth</td><td>-0.010**<br>(0.004)</td><td>0.016*<br>(0.009)</td><td>-0.020**<br>(0.008)</td><td>0.012*<br>(0.007)</td><td>0.013**<br>(0.007)</td></tr><tr><td>GDP growth</td><td>0.001<br>(0.001)</td><td>0.007**<br>(0.003)</td><td>-0.003<br>(0.003)</td><td>-0.001<br>(0.002)</td><td>-0.005**<br>(0.002)</td></tr><tr><td>LQ<br>Services</td><td>-0.387***<br>(0.060)</td><td>-0.859***<br>(0.142)</td><td>0.040<br>(0.126)</td><td>-0.567***<br>(0.086)</td><td>-0.035<br>(0.114)</td></tr><tr><td>Observations</td><td>5443</td><td>894</td><td>897</td><td>1838</td><td>1814</td></tr><tr><td>R<sup>2</sup></td><td>0.218</td><td>0.407</td><td>0.488</td><td>0.284</td><td>0.169</td></tr><tr><td>F Statistic</td><td>180.151***</td><td>70.900***</td><td>98.722***</td><td>85.737***</td><td>43.232***</td></tr></tbody></table>

Note: *p < 0.1; **p < 0.05; ***p < 0.01.

Models' fixed effects are two-way (NUTS2-year).

VL: Virtuous Loop regions. CI: Carbon-Intensive Trap regions.

ET: Evolution Trap regions. HE: High-Emission Trap regions.

by their existing industrial structure, which reduces their capacity to diversify their economies and to adopt low-carbon technologies.

Sub-sampling shows interesting heterogeneity (Table 2). An interesting relation we discover concerns regional industrial capital renewal capacities, which in some cases seem to help reduce emission levels, in particular for virtuous regions. This group of regions, along with High-Emissions regions, are the only one where the coefficient is significant and negative, which indicates that they manage to adopt renewable technologies and integrate them into new productive capital and infrastructure, facilitating the low-carbon transition. At the same time, virtuous regions show generally low coefficients of all economic variables, contrary to the other groups. The analysis of interaction terms (Table A2) reveals statistically significant and positive differences in the effect of GDP per capita for Carbon-Intensive Trap regions. This result reinforces the interpretation that, in these territories, higher levels of economic affluence coincide with persistent emission-intensive development trajectories. Economic wealth, in such cases, does not automatically facilitate decarbonisation but often coexists with structural rigidities and consumption patterns that amplify carbon outputs. By contrast, regions classified as High-Emission Traps exhibit a comparatively

more negative effect of GDP per capita relative to Virtuous Loop regions. This suggests that economic development in these regions is more strongly associated with diversification efforts and increased investment in low-carbon infrastructure and technologies.

Furthermore, the effect of capital renewal also varies significantly across typologies. In Carbon-Intensive Trap and Evolution Trap regions, the replacement of productive capital does not generate meaningful reductions in emissions, indicating that new investments often reproduce carbon-intensive structures rather than initiate transformative change. This finding highlights the necessity of reorienting capital formation towards sectors and technologies aligned with climate neutrality, particularly in territories where entrenched industrial models continue to shape developmental trajectories.

## Analysis of demographic, institutional and social determinants of emissions per capita

The analysis of demographic, institutional and social variables sheds light on the ways in which regional characteristics influence CO₂ emissions per capita. First, population density is a consistently significant and negative determinant of emissions per capita, with coefficients ranging from -0.186 to -0.793. This suggests that denser re-
gions are better positioned to mitigate emissions by effi-
ciently using resources and infrastructure. Urbanisation
often facilitates economies of scale in energy use, en-
hances public transport systems and reduces reliance on
individual vehicles. Dense areas may also concentrate in-
vestments in energy-efficient infrastructure, such as dis-
strict heating and green buildings, further contributing to
reduced emissions per capita. In virtuous regions, the neg-
ative effect of population density is especially pronounced
(−1.225). This finding suggests that these regions not only
benefit from urbanisation but also actively manage it to
achieve low-carbon outcomes. Examples may include inte-
grated public transport systems, urban planning that pri-
oritises walkability and stringent environmental regula-
tions in urban zones. Conversely, in Carbon-Intensive Trap
and Evolution Trap regions, the coefficient is less negative
(respectively −0.666 and −0.160), indicating that the poten-
tial benefits of density are limited by structural and insti-
tutional barriers. For instance, these regions might have
older, less efficient infrastructure or a reliance on fossil fu-
els for transportation and energy needs, which diminishes
the mitigating effect of density on emissions.

Population growth, in contrast, shows weaker and less
consistent relationships with emissions. The coefficients
are generally non-significant or slightly different from zero,
suggesting that population increases do not uniformly lead
to higher emissions per capita. In some contexts, growing
populations might coincide with investments in modern,
efficient infrastructure or shifts in economic activities to-
wards less carbon-intensive sectors, which could counter-
act the expected rise in emissions. However, this relation-
ship appears highly context-dependent. For example, re-
gions with proactive urban planning and resource-efficient
systems may accommodate population growth without
a proportional increase in emissions, while others might
struggle with infrastructural or political inertia.

Quality of government is generally not a statistically
significant determinant of emissions per capita. However,
Table A2 shows how different its effect is across categories
of regions, with trapped regions seemingly benefiting more
from improvements in government quality. This empha-
sises the critical role of governance in shaping a region's
carbon trajectory, especially for trapped regions. Regions
with high quality of government are likely to have the ad-
ministrative capacity and political will to implement ef-
fective environmental policies. This could include enfor-
ing regulations on emissions, promoting renewable energy
adoption and supporting green innovation through sub-
sidies or public-private partnerships. Strong institutions
may also facilitate stakeholder engagement and long-term
planning, which are essential for successful low-carbon
transitions. For example, regions with robust governance
frameworks might attract greater investment in renewable
energy projects and energy-efficient technologies. In con-
trast, regions with weak quality of government might face

challenges such as regulatory capture by high-emission in-
dustries, corruption or policy fragmentation, which hinder
their ability to decarbonise.

The effects of education and R&D investments on emissions are more complex and somewhat counterintuitive. Coefficients for these variables are generally non-significant or slightly positive, suggesting that higher levels of education and innovation capacity do not automatically translate into lower emissions per capita. One possible explanation is that regions with advanced education systems and robust R&D ecosystems often host knowledge-intensive industries and affluent populations, both of which can contribute to higher energy consumption and emissions. For instance, highly educated populations may engage in lifestyle choices that are resource-intensive, such as frequent travel or the use of high-energy appliances. Similarly, R&D activities, while crucial for developing green technologies, often involve energy-intensive processes, particularly in industrial and laboratory settings. These dynamics highlight the importance of directing education and R&D efforts specifically towards sustainability-focused initiatives to ensure their potential for reducing emissions is fully realised.

Characterising regional disparities through
the “regional carbon trap” status

The results from the analysis highlight significant dispari-
ties across the four types of regional carbon trap statuses.
Each category exhibits distinct characteristics and chal-
lenges related to CO₂ emissions, influenced by varying de-
mographic, institutional and social factors.

Virtuous regions stand out for their ability to decouple
economic growth from carbon emissions. Population den-
sity exerts the strongest negative impact in these regions
(−1.225), which demonstrates their capacity to harness ur-
banisation to achieve sustainable outcomes. High-density
settlements are typically supported by well-developed in-
frastructure and accessible public transport networks,
which reduce reliance on private vehicles and lower emis-
sions per capita. The quality of government also con-
tributes significantly to these dynamics, though its role
appears more complementary than decisive. In these re-
gions, strong governance frameworks facilitate the deploy-
ment of effective decarbonisation strategies, support in-
vestments in renewable energy and enable the integration
of green infrastructure. However, the fact that the qual-
ity of government has an even stronger effect in trapped
regions suggests that, while it is an important enabler, it
does not suffice on its own to trigger a low-carbon tran-
sition. In virtuous regions, it operates in conjunction with
other factors — such as a strong alignment between ed-
ucation, R&D, and sustainability goals — which together
allow these regions to direct innovation and technological
progress towards reducing carbon intensity.
Carbon-Intensive Trap regions face entrenched challenges due to their historical reliance on carbon-intensive industries. The positive and statistically significant coefficients for GDP per capita and manufacturing specialisation underscore the persistent reliance of these regions on fossil-fuel-based economic structures. While some initiatives have aimed to modernise infrastructure, the influence of capital renewal remains limited or statistically negligible. This pattern suggests that recent investments in advanced technologies have neither reached sufficient scale nor disrupted the deeply embedded carbon-intensive systems that characterise these economies.

The observed negative coefficient for population density (−0.666), although less pronounced, points to a limited capacity of urbanisation to generate meaningful environmental or economic dividends. Several factors may account for this, including antiquated transport and energy systems or ineffective spatial planning, both of which constrain the efficiency gains typically associated with denser urban environments. Furthermore, the quality of government in these regions frequently proves inadequate to support the design and implementation of ambitious, transformative policies. This institutional weakness reinforces path dependencies and contributes to the continued dominance of high-carbon development trajectories (Table A2).

High-Emission Trap regions share similarities with Carbon-Intensive Traps but show some signs of transition. In these regions exhibit, GDP per capita and manufacturing specialisation both contribute significantly to their carbon footprints. However, the lack of a strong negative relationship with capital renewal and R&D investments suggests that these regions struggle to align economic and innovation capacities with decarbonisation objectives. The significant negative effect of quality of government effects points to governance gaps that hinder the ability of these regions to manage their transition. Without targeted interventions, such as policies encouraging exnovation — the proactive dismantling of obsolete, carbon-intensive infrastructures — these regions risk remaining locked into their current high-emission pathways.

Evolution Trap regions exhibit a more dynamic and complex profile. Unlike other categories, GDP growth in these regions is not associated with increased emissions. This suggests that these areas may be at a tipping point where economic activities begin to shift towards less carbon-intensive sectors. The weaker relationship between industrial specialisation and emissions indicates that these regions either lack a strong manufacturing base or are starting to diversify into greener industries. Population density does not show as strong a mitigating effect as in virtuous regions, but the relatively lower carbon intensity of economic activities provides these regions with opportunities to adopt leapfrogging strategies. By bypassing traditional carbon-intensive development stages, Evolution Trap regions could directly invest in renewable energy

systems, modern transport infrastructure, and sustainable urban planning. These efforts would enable them to transition into low-carbon trajectories more rapidly than other regions.

## Discussion and conclusion
### Key findings and their implications

This study set out to explore the persistence of CO₂ emissions across European regions through the lens of path dependencies and socio-technical lock-ins. By introducing the concept of the “regional carbon trap,” the aim was to examine how inherited economic structures, industrial specialisation, and institutional frameworks shape the dynamics of emissions reduction. Specifically, we sought to understand the factors that enable certain regions to decarbonise effectively while others remain trapped in high-carbon trajectories. By categorising regions into four typologies — Virtuous Loop, Carbon-Intensive Trap, High-Emission Trap, and Evolution Trap — this study sheds light on the varied paths regions are taking towards or away from carbon-intensive trajectories. This regional differentiation offers a new perspective on the mechanisms of carbon lock-ins and enriches the literature on path dependency (Martin, 2010; Unruh, 2000) and regional development traps (Diemer et al., 2022; Balland and Boschma, 2024).

The findings underscore the critical role of regional characteristics in shaping carbon trajectories. Consistent with the literature on path dependency and lock-ins (Martin and Sunley, 2006; Grabher, 1993), the results reveal that regions with rooted industrial specialisations and weak institutional frameworks face significant barriers to decarbonisation. In this sense, the typologies developed in this study provide a clear framework for understanding regional disparities. Virtuous Loop regions exemplify how effective governance and the strategic use of urban density can drive decarbonisation. The strong negative impact of population density in these regions aligns with studies emphasising the role of urbanisation in facilitating economies of scale, reducing transportation emissions and enabling efficient infrastructure use (Boschma, 2015; Geels and Schot, 2007). These regions highlight how coordinated investments in renewable energy, public transport systems and green technologies can decouple economic growth from emissions, a finding that reinforces the importance of strong quality of government (Rodríguez-Pose and Ketterer, 2020).

In contrast, Carbon-Intensive Trap regions demonstrate how path dependency perpetuates carbon lock-ins. These regions, characterised by high GDP per capita and industrial specialisation, remain heavily reliant on fossil-fuel-intensive industries. Despite investments in manufacturing capital renewal, the weak impact of these efforts suggests that such investments are often insuffi-
cient without structural reforms and policy interventions. This aligns with Turnheim (2022), who emphasised the importance of “exnovation” — the proactive dismantling of carbon-intensive infrastructures — in overcoming these entrenched dependencies.

High-Emission Trap regions, while exhibiting some progress in reducing emissions, highlight the challenges of aligning economic and innovation capacities with decarbonisation objectives. The absence of significant effects from R&D investments in these regions raises questions about the effectiveness of innovation strategies when not explicitly geared towards sustainability. This finding resonates with studies suggesting that R&D investments must be directed towards green technologies to yield substantial emissions reductions (Truffer and Coenen, 2012; Luo and Zhang, 2022). Evolution Trap regions present a unique case, with GDP growth showing no significant association with increased emissions. This decoupling suggests potential for these regions to transition into low-carbon pathways through diversification and leapfrogging strategies. These findings complement literature advocating for tailored policy interventions to help such regions bypass traditional carbon-intensive stages of development (Unruh and Carrillo-Hermosilla, 2006; Trippl et al., 2020).

Another result is that quality of government emerges as a critical determinant of regional carbon trajectories. Regions with robust governance frameworks demonstrate greater capacity for decarbonisation, consistent with Rodríguez-Pose and Wilkie (2017), who emphasised the role of institutions in shaping regional resilience and transition capabilities. In Virtuous Loop regions, strong institutions have likely played a role in fostering stakeholder collaboration, enforcing environmental regulations and attracting investments in green technologies. Conversely, weaker quality of government in Carbon-Intensive and High-Emission Trap regions exacerbates their reliance on high-carbon trajectories, which is in line with the findings of Tinta (2022) and Obobisa et al. (2023).

The complex relationship between education, R&D investments and emissions highlights the dual-edged nature of these factors. While education and innovation capacities are crucial for developing green technologies, their benefits are not automatic. Regions hosting affluent, highly educated populations or energy-intensive R&D activities may exhibit higher per capita emissions, reflecting consumption-driven carbon footprints (Kerkhof et al., 2009). This underscores the need for aligning educational and innovation systems with sustainability goals to harness their potential for emissions reduction.

Industrial specialisation in Carbon-Intensive and High-Emission Trap regions underscores the persistence of path dependencies. These regions' reliance on fossil-fuel-based industries aligns with studies highlighting the role of functional lock-ins in perpetuating carbon-intensive systems (Grabher, 1993; Hassink, 2010). The limited impact of man-

ufacturing capital renewal in these regions further supports the argument that structural inertia is a significant barrier to transition, as highlighted by Apajalahti and Kungl (2022).

## Theoretical contribution

This study introduces the “regional carbon trap” as a geographical extension of the concept of carbon lock-ins (Unruh, 2000). While carbon lock-ins traditionally emphasise systemic inertia within national or sectoral socio-technical systems, our framework highlights the critical role of subnational disparities and local contexts in perpetuating or overcoming these trajectories. By focusing on regional typologies and their distinct characteristics, this study provides a granular understanding of how path dependencies and lock-ins manifest unevenly across space and the consequences on the ecological transition of regions (Newey and Coenen, 2022). This territorial perspective stresses that decarbonisation is not merely a technical or economic challenge but a deeply embedded geographical one, shaped by the historical, economic and institutional heritage of regions.

The typology of regional carbon traps advances our theoretical understanding by emphasising the heterogeneity of carbon trajectories. This approach complements existing theories of regional path dependence (Martin, 2010; Boschma, 2015) by integrating insights from economic geography with the concept of carbon lock-ins. It demonstrates that the mechanisms sustaining carbon-intensive trajectories, such as industrial specialisation, institutional inertia and socio-technical dynamics, are not uniform but instead vary significantly depending on regional contexts.

The findings of this study build on and extend the seminal work on path dependence and lock-ins by David (1985), Arthur et al. (1987) and Grabher (1993), which identified cognitive, functional and political mechanisms as drivers of regional inertia. Our analysis deepens this understanding by demonstrating how these mechanisms intersect with regional disparities, which creates distinct challenges for different types of regions. For instance, the weak impact of capital renewal in Carbon-Intensive Trap regions highlights the persistence of functional lock-ins, where outdated infrastructure and industrial structures inhibit transitions. Similarly, the role of quality of government in Virtuous Loop regions demonstrates how political lock-ins can be overcome through effective governance, which is in line with findings by Rodríguez-Pose and Wilkie (2017). Moreover, the study contributes to theories of regional economic transitions by linking industrial renewal and diversification strategies to emissions trajectories. It highlights how diversification can serve as a pathway out of carbon-intensive lock-ins, particularly for Evolution Trap regions, complementing research on regional resilience and path-breaking (Boschma and Frenken, 2018; Trippl et al., 2020).
## Policy implications

Effective decarbonisation requires the formulation of spatially differentiated transition strategies that account for the diversity of regional development trajectories. This section outlines a structured policy framework articulated around three distinct yet interdependent strategies: exnovation, diversification and leapfrogging. Each strategy addresses a specific type of regional carbon trap and targets corresponding structural and institutional barriers. The success of these interventions depends on the coherence of policy design, the strength of multilevel governance arrangements, the administrative capacity of regional institutions and their alignment with the overarching objectives of EU climate and Cohesion policy.

First, exnovation constitutes a foundational strategy for Carbon-Intensive Trap regions, which remain structurally embedded in fossil-based development trajectories and exhibit persistent institutional inertia. These regions face entrenched technological lock-ins, legacy infrastructures and socio-political coalitions that sustain high-emission regimes. Effective intervention requires the implementation of deliberate and coordinated policy frameworks designed to phase out carbon-intensive activities while enabling structural realignment. Exnovation must be understood as a multidimensional process. It entails the elimination of subsidies that maintain uncompetitive polluting sectors, the abrogation of regulatory arrangements that disproportionately benefit high-emission industries and the redefinition of regional planning priorities around low-carbon alternatives.

This process demands more than technological substitution; it calls for institutional innovation, governance restructuring, and the redistribution of material and symbolic resources across sectors. The EU's Just Transition Mechanism (JTM) exemplifies an integrated policy instrument capable of supporting such transitions. It allocates financial assistance for the decommissioning of fossil fuel infrastructures, facilitates labour market reconfiguration through retraining programmes and fosters institutional adaptation at the regional level. The case of Silesia in Poland illustrates the operational potential of this mechanism: JTM funding has supported the closure of lignite mines, the redevelopment of former industrial zones into renewable energy platforms and the deployment of training initiatives to reposition the regional workforce (Męczyński and Ciesiółka, 2024). However, successful exnovation strategies require strong institutional coordination, inclusive stakeholder engagement and sustained political commitment to mitigate the distributive risks associated with structural change (Rodríguez-Pose, 2013; Oei et al., 2019).

Second, diversification constitutes an appropriate strategy for High-Emission Trap regions that exhibit dynamic but carbon-intensive industrial structures. These regions often combine productive capacities and technological potential

with institutional rigidities that impede structural transformation. To initiate a transition towards low-carbon development, regional actors must reconfigure existing assets and foster the emergence of new specialisations that break with legacy sectors. Diversification requires more than the gradual adaptation of incumbent industries. It demands the creation of institutional and infrastructural conditions that support technological branching, promote sectoral recombination and enable shifts in labour market orientations.

Effective diversification strategies rest on the development of regional innovation systems that encourage experimentation, absorptive capacity and inter-organisational collaboration (Grillitsch et al., 2018). Public policy plays a crucial role in this process. Instruments such as the European Regional Development Fund and Horizon Europe can provide targeted support for green industrial renewal, facilitate the formation of thematic pilot zones and finance collaborative research initiatives focused on climate-oriented technologies. Within this policy architecture, the Smart Specialisation approach offers a robust strategic framework (Molica and Marques Santos, 2024). It enables regions to articulate transformation agendas that are grounded in local capacities while aligned with broader societal missions (Foray et al., 2015). Through structured entrepreneurial discovery processes and selective policy prioritisation, Smart Specialisation guides the mobilisation of endogenous resources towards sustainable growth paths (Molica, 2025). In this sense, diversification operates as both a driver of innovation and an instrument of territorial cohesion. It contributes to the redefinition of regional development models, encourages institutional adaptation and supports the long-term reconfiguration of economic structures away from high-emission trajectories.

Third, leapfrogging defines a strategic policy option for Evolution Trap regions, which remain in an intermediate state of transition. These territories have not achieved a stable decarbonisation trajectory, yet maintain the institutional and infrastructural foundations necessary to absorb advanced low-carbon technologies. Rather than progressing through sequential stages of industrial development, these regions are in a position to integrate cutting-edge solutions in areas such as renewable energy production, sustainable mobility and low-carbon urban infrastructure. This mode of transition avoids the reproduction of carbon-intensive development paths and enables a more rapid shift towards climate neutrality.

To operationalise leapfrogging, regional policies must establish conditions that support the acquisition of technological competences and the diffusion of innovative practices. This process presupposes the presence of institutional coordination mechanisms, the mobilisation of long-term investment and the establishment of knowledge infrastructures capable of translating external expertise into regionally embedded solutions (Yap et al., 2022). From
this perspective, European policy instruments such as In-
terreg Europe offer structured platforms to support these
dynamics (Moghadam et al., 2023). These programmes fa-
cilitate transregional policy transfer, the co-development
of demonstration projects and the deployment of systemic
innovations in areas including energy networks, mobility
systems and sustainable construction.

Challenge-oriented Smart Specialisation provides an additional strategic layer. This approach allows regional authorities to define priorities that reflect both local specificities and overarching environmental goals. Through the alignment of mission-driven policies with regionally grounded capabilities (Molica et al., 2025), Smart Specialisation supports the integration of leapfrogging into broader innovation strategies. In less-developed regions that do not exhibit entrenched carbon lock-in but lack diversified economic structures, such strategies can promote inclusive transformation. Leapfrogging thus becomes a mechanism for advancing both decarbonisation and territorial cohesion while enhancing the strategic autonomy of peripheral regions within the European innovation landscape.

Limitations, future research and broader perspectives

While this study provides significant insights into regional disparities in carbon emissions and the mechanisms of carbon traps, certain limitations warrant ac knowledgment. First, the reliance on NUTS-2 level data, though practical for comparative analysis across Europe, limits the granularity of findings. Sub-regional and sub sectoral dynamics, local variations and informal institu tional practices may be underrepresented, potentially ob scuring finer understanding of carbon lock-ins. Further more, the cross-sectional nature of some analyses con strains the ability to directly infer causality between ob served relationships, such as the interplay between qual ity of government and decarbonisation outcomes. While the econometric methods employed offer robust correla tions, longitudinal or experimental approaches are needed to establish causal pathways.

Future research should address these gaps by incorpo-
rating detailed case studies of specific regions. Such inves-
tigations could clarify localised mechanisms behind car-
bon lock-ins and successful transitions. Longitudinal stud-
ies are also essential to capture temporal changes in emis-
sions trajectories and the impact of policy interventions
over time. Finally, the integration of qualitative methods,
including interviews with policymakers, industry stake-
holders and civil society actors, can complement quant-
titative approaches. In particular, future work could ben-
efit from engaging with the socio-technical configuration
analysis framework (Markard et al., 2012; Binz and Truf-
fer, 2017; Heiberg et al., 2022), which provides a more de-

tailed view of institutional effects beyond generic governance indicators. This type of approach could help capture how configurations of actors, technologies and institutions become aligned or destabilised across space and time.

The findings of this study underscore the critical impor-
tance of addressing regional disparities to achieve broader
decarbonisation goals. Regional carbon traps not only hin-
der the progress of specific territories but also pose a
challenge to the collective objectives of the European
Green Deal and global climate commitments. Breaking free
from these traps requires a multifaceted approach that
combines tailored policies, institutional strengthening and
strategic investments in innovation. Achieving carbon neu-
trality by 2050 will necessitate a profound transformation
across all regions, with particular attention to those cur-
rently stuck in carbon-intensive pathways. This process
is not merely technical but also inherently political and
socio-economic, requiring the alignment of diverse stake-
holders and the redistribution of resources to foster equi-
table transitions. The European Union's focus on cohesion
and place-sensitive policies offers a framework for ensur-
ing that no region is left behind in the shift towards a low-
carbon future.

Endnotes

1 In particular, we include the sectors classified as “J — Infor- mation and communication,” “K — Financial and insur- ance activities”; “M-N — Professional, scientific and tech- nical activities/Administrative and support service activi- ties”; “O-Q — Public administration and defence; compul- sory social security/Education/Human health and social work activities” and “R-U — Other service activites” of the NACE 10-sector disaggregation classification.

Supplementary material

Supplementary material is available at *Cambridge Journal of Regions, Economy and Society* online.

Acknowledgements

Sébastien Bourdin acknowledges that this paper is pub-
lished with the support of the project "SAGES: Spatial Anal-
ysis of Growth, Environment and Sustainable Well-being,"
project code CF 20/27.07.2023, financed through the Na-
tional Recovery and Resilience Plan for Romania within
project call — PNRR-III-C9-2023-I8 PNRR/2023/Component
9/Investment 8. [Finanțat de Uniunea Europeană —
NextGenerationEU].

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