1. Introduction
The accelerated pace of digital transformation and the imperative of sustainable development have increasingly necessitated a robust response from the labour market across the European Union. Within this framework, the interplay between academic innovation and industrial transformation has garnered substantial scholarly and policy attention, as these synergies are recognised as pivotal drivers of economic resilience and employment stability. Prior research has demonstrated the capacity of academic–industry collaboration to facilitate technological advancement, knowledge dissemination, and the enhancement of workforce competencies [1,2,3,4]. Moreover, investigations have put emphasis on the role of digitalisation and strategic investment in instigating industrial change, thereby modelling employment trajectories within dynamic economic sectors [5,6,7]. Nonetheless, despite the expansion of the literature on this field, the specific mechanisms through which academic–industrial synergies contribute to sustainable employment, particularly within the heterogeneous labour markets of the European Union, remain insufficiently elucidated and subject to debate. In spite of the acknowledgement of interdisciplinary connections in both policy and research agendas, there remains a conceptual fragmentation with regard to the impact of academic innovation and industrial transformation on sustainable labour markets. Hence, a more comprehensive theoretical anchoring of these interactions is needed to meaningfully frame this relationship within the evolving context of the employment trilemma.
Whereas certain scholars contend that digitalisation may intensify labour market instability and deepen socio-economic inequalities [8,9,10], others claim that efficacious collaboration between academia and industry can mitigate such risks by enabling continual skills development and fostering adaptive innovation [11,12,13]. These contrasting viewpoints expose a significant gap in the current body of literature regarding the conditions and extent to which academic–industrial partnerships sustain labour market stability, especially in the context of ongoing digital and green transitions. Furthermore, extant studies frequently concentrate either on technological innovation or employment outcomes in isolation, thereby neglecting to integrate sustainability metrics or consider broader socio-economic implications within the EU.
This divergence calls for a clearer conceptual delineation between digital transformation, innovation systems, and labour market sustainability. Moreover, despite the frequent recurrence of the term sustainability in the scientific discourse, its application to employment-related outcomes still remains inconsistently theorised. Consequently, this paper responds to that need by advancing a unified framework aiming to address this research deficit by systematically evaluating the impact of academic innovation and industrial transformation, buttressed by investment and digitalisation, on sustainable employment across the European member states. The methodology applied in this research to fulfil its primary objective is a mixed-methods approach, which integrates quantitative sustainability parameters together with qualitative case studies that rigorously illustrate the ways such collaborations energise the local labour market and contribute to sustainable development goals. Thus, the main research question guiding this investigation is formulated as follows: RQ1: To what extent can synergies between academic innovation and industrial transformation nurture long-term employment stability within the EU-27 member states?
The anticipated findings aspire to inform regulatory authorities, academics, and industrial stakeholders, but also to place emphasis on the strategic necessity of nurturing synergistic partnerships to effectively navigate digitalisation challenges and foster equitable and resilient labour markets. Since the main focus is on the multi-layered interactions among innovation, transformation, and employment, this study situates itself at the intersection of innovation studies, labour economics, and sustainability research. In doing so, this paper seeks to provide analytical depth and policy relevance to this field of knowledge.
Hence, this paper’s value resides in its comprehensive integration of digital transformation, sustainability assessment, and labour market analysis, advancing interdisciplinary knowledge of academic–industry synergies within the sustainability discourse while bridging critical theoretical and empirical gaps across innovation studies, labour economics, and sustainable development.
Against this backdrop, the subsequent sections unfold with the literature review, which canvasses the pertinent studies on digital transformation, sustainability metrics, and academic–industry collaboration, followed by the third section, which delineates the research methodology and encompasses data sources and analytical frameworks. The fourth section unveils the empirical findings alongside case study evaluations, while the discussion part contemplates the ramifications of these insights for labour market stability and sustainable development within the European Union. Finally, the paper culminates in the conclusion section, which synthesises key contributions, acknowledges limitations, and proposes avenues for further inquiry.
2. Literature Review
2.1. The Employment Trilemma as a Theoretical Foundation
The employment trilemma framework represents the essential conflicts that decision-makers and labour market participants encounter when they try to achieve the three objectives of competitiveness, innovation, and social inclusion. Additionally, the employment trilemma adds to the economic literature, which analyses policy trade-offs like the impossible trinity of monetary economics, because it describes three conflicting objectives that policymakers must manage [14]. Other studies addressing the employment field show that economic development and market competitiveness generate important challenges for maintaining fair social outcomes and innovative capabilities [15,16]. Our framework positions academic innovation, industrial transformation, and sustainable employment as key factors that shape the employment trilemma through which their combined effects might resolve conflicts between economic growth and social well-being. Thus, this paper uses this theoretical framework to show how these three dimensions intersect, as shown in Figure 1.
Most previous studies explore various aspects of digitalisation, sustainability, and collaborative ecosystems but fail to provide an integrative review that directly examines the relationship between these three concepts to capitalise on academic innovation, stimulate industrial transformation, and maintain employment sustainability within a framework. Hence, we provide a unique framework to do this by tracing the processes of how academic innovation leads to industrial transformation, which, in due course, develops employment sustainability within the various EU-27 member states. Moreover, we use the lens of the employment trilemma to offer clarity as to how relationships between interconnected interdependencies can be used to influence policy and strategic judgments, with respect to the trade-offs of providing competitiveness, innovation, and social inclusion concurrently.
2.2. Academic Innovation: Anchoring Ecosystem-Level Change
This section is grounded in the above theoretical framework and carries out an examination on the role of academic innovation as the initial driver in addressing the employment trilemma. Specifically, it takes into account the contribution of universities and research institutions to the advancement of digital and sustainable capabilities and identifies the primary pathways through which innovation enters the wider economic landscape. On one hand, the intersection of digital transformation, sustainability, and collaborative ecosystems has become a central point in contemporary organisational and academic research. Hence, scholars have increasingly emphasised the important role of internal culture and external partnerships in nurturing innovation and strategic adaptability. To begin with, Mišíková and Jankelová [3] put emphasis on the significance of corporate culture as a central pillar in enabling employee well-being and productivity in the face of digital transformation. Their work advocates for the supportive and resilient potential of cultures to act as catalysts for digital upskilling, while also helping employees to navigate the complexities of hybrid environments and maintain work–life boundaries. In a related vein, Al-Qaruty et al. [17] find a statistically significant link between digital transformation and strategic sustainability, mediated by conscious leadership, particularly within the financial services sector. Consequently, this affirms the premise that digital transformations must be implemented by mindful leadership and organisational coherence.
Beyond organisational boundaries, the role of academia in innovation ecosystems has drawn considerable attention from scholars. For instance, Pervan et al. [18] argue that close cooperation between educational institutions and firms facilitates knowledge and technology transfer, particularly through the adoption of open innovation models that mitigate constraints such as limited resources and expertise. In contrast, Daradhek [19] nuances this view by illustrating that while moderate academic–industry collaboration is mutually beneficial, excessive cooperation may inhibit scientific innovation by discouraging open peer exchange and weakening ethical vigilance among researchers.
Other studies probe deeper into the mechanism of knowledge transfer, pointing to co-publication and co-patenting as effective conduits for open innovation, with prior entrepreneurial experience further enhancing academic–industry synergies [1]. Similarly, Bagchi et al. [20] indicate that contract research, joint research, and institutional support networks significantly enhance transnational research outputs. These findings are echoed by Esangbedo et al. [21], who claim that robust policy frameworks are mandatory to institutionalise academic–industry collaboration, notably for AI integration in educational settings. Additionally, the longitudinal value of such collaborations is further explored by Rollnik-Sadowska [22], who asserts that the temporal extension of partnerships generates cumulative mutual benefit and recommends embedding dual-skilled facilitators to bridge sectoral divides and promote smoother knowledge exchange. Conversely, building upon Hoffman’s [23] foundational framework, Huang and Xiong [24] challenge the epistemological assumptions of collaboration and argue that entrenched divergences in knowledge systems and market-driven incentives hinder genuine ontological consensus and constrain transformative knowledge production. The work of Bradbury et al. [11] offers a more practice-oriented perspective and identifies factors such as strategic alignment, clearly defined roles, and interpersonal trust as key drivers of successful academic–industry cooperation. Nonetheless, their results also hint at underlying tensions related to the recognition of individual contributions and suggest that formal structures alone may not fully account for the complexities of collaborative dynamics.
2.3. Industrial Transformation: Operationalising Innovation
Building on the contributions of academia, industrial transformation is the operational stage during which knowledge and innovation are absorbed, reshaped, and implemented through technological upgrades, corporate structures, and sustainability objectives. From a technological standpoint, Cao et al. [25] demonstrate how extended reality (XR) serves as a key enabler of Industry 5.0. Their application includes virtual, augmented, and mixed reality, and within the manufacturing sector, it reveals potential environmental benefits through virtual prototyping, ergonomic simulations, and cognitive support, thus aligning technological advancement with ecological sustainability.
For those reasons, the industrial sector represents both a recipient and amplifier of academic innovation, contingent upon supportive cultures and strategic governance. In this regard, the main factors that play a mediating role in enabling industries to digest academic knowledge and apply it toward sustainable outputs are the adaptive organisational culture, strong leadership, and openness to experimentation. Likewise, these developments remain a significant turning point in linking innovation to employment as well.
2.4. Towards Employment Sustainability: Strategic Outcomes and Social Inclusion
Once academic innovation is channelled into transformative industrial practices, the resulting impacts on the labour market can either reinforce or undermine employment sustainability. Overall, the extant literature converges on the notion that digital transformation, when supported by adaptive corporate cultures and strategic partnerships, holds the potential to advance organisational sustainability and innovation. Yet, the nature and depth of academic–industry cooperation must be carefully managed to balance mutual benefit with the preservation of academic rigour and autonomy.
Furthermore, digitalisation and sustainability pressures reshape the structure of work and demand new skills, as well as adaptive employment models. In this respect, employment sustainability becomes a policy target that must reconcile innovation-driven competitiveness with social inclusion, a key tenet of the employment trilemma.
In conclusion, while many existing studies offer fragmented insights into digitalisation, innovation ecosystems, and employment implications, few integrate these components into a holistic model. The current review reveals a missing synthesis that logically connects academic innovation as the upstream driver of industrial transformation, which in turn determines the conditions for sustainable employment. Moreover, our study addresses that gap by proposing and applying a conceptual framework rooted in the employment trilemma, thus offering a novel lens to examine how interconnected forces can shape inclusive, competitive, and innovative labour markets across the EU-27.
3. Materials and Methods
The main focus of this study is to carry out an exploration of how the deep interrelation of academia, industry, and sustainability affects youth employment outcomes in the EU-27 from 2005 to 2023. Thus, based on the foregoing panel data studies on growth and labour market dynamics, the following hypothesis was tested:
Increased synergies between business R&D and tertiary educational attainment amplify employment rates through sustainable growth channels.
3.1. Research Design
The use of a macro-level dynamic panel model with fixed effects finds justification in the conceptual structure of the employment trilemma, which articulates academic innovation, industrial transformation, and sustainable employment within a relationship of tension system across various national contexts. In line with this framing, therefore, the present study prioritises country-level analysis to capture how structural changes in systems for innovation, as well as environmental performance, influence youth employment outcomes over time. Harmonised indicators from Eurostat, such as tertiary education attainment, business enterprise R&D intensity, energy productivity, and greenhouse gas emissions, ensure full temporal and geographic coverage for all EU-27 member states between 2005 and 2023. The addition of a lagged dependent variable captures some degree of inertia in the labour market; fixed effects control for institutional and historical differences between national innovation regimes as emphasised in the literature on national systems of innovation and absorptive capacity.
Alternative approaches, which would have implied the use of sectoral or micro-level data, were discarded due to the limitations attached to these sources in terms of availability over the full time horizon and sample. Co-patenting digital adoption at firm-level, or green employment by occupation, is not available for most countries, and where it exists is fraught with inconsistencies across time and hence not suitable for longitudinal analysis striving to find EU-wide structural dynamics. Even more, although System GMM offers advantages in dealing with endogeneity, the estimator suffers from overfitting and instrument proliferation in this panel with a relatively small cross-sectional dimension (N = 27) and moderate time series (T = 19), hence compromising statistical validity. Thus, the chosen empirical strategy demonstrates a methodical attunement not only with the theoretical framework but also with the available data’s scope, offering a model that is both robust and easily manageable for evaluating the manner in which cross-sectoral synergies affect employment sustainability at the European level.
Figure 2 depicts the conceptual flow that informs the empirical analysis. Academic innovation, measured by the annual increase in tertiary education attainment, is positioned as the primary driver of absorptive capacity within national innovation systems. This enhanced capacity enables firms to translate higher business research and development intensity into job creation, a relationship formalised through the interaction term ΔED × ΔBERD. The diagram thus signals that research-led industrial change supports youth employment only when the workforce possesses adequate advanced skills, reflecting insights from the literature on national systems of innovation and absorptive capacity.
The model further embeds youth employment within a wider structural context. Aggregate demand is represented by real GDP growth, while ecological pressure is captured through changes in energy productivity and the level of greenhouse gas emissions. These controls acknowledge that labour market outcomes are simultaneously influenced by macroeconomic cycles and environmental constraints. Crisis dummies for the 2008–2009 financial shock and the 2020–2021 pandemic isolate the effects of extraordinary disturbances, ensuring that estimated relationships reflect underlying structural dynamics rather than one-off shocks. Collectively, the figure illustrates an integrated pathway linking education-driven innovation, private research effort, and sustainable employment across the European Union.
3.2. Operationalisation of Variables
The selection of proxies in this study is theoretically anchored in the systemic view of innovation and employment developed within the employment trilemma framework. Academic innovation is proxied by changes in tertiary education attainment, which reflects not only the expansion of higher education but also the structural shift towards knowledge-intensive human capital. This indicator aligns with the concept of absorptive capacity, a cornerstone of innovation-system theory, which posits that the ability of an economy to benefit from technological change is conditional upon the skills and learning capabilities of its workforce. From this perspective, educational attainment is not merely a demographic metric but a mechanism through which innovation becomes socially embedded and economically productive, particularly for the younger cohorts entering the labour market.
Industrial transformation is represented through the evolution of business enterprise R&D intensity, capturing the degree to which firms commit to innovation-driven restructuring. This aligns with Schumpeterian dynamics, where private sector investment in research and development serves as a key driver of technological renewal, new business models, and sectoral reconfiguration. To account for sustainability pressures, two ecological indicators are integrated: energy productivity, reflecting shifts in efficiency and resource optimisation, and greenhouse gas emissions, capturing the environmental costs of industrial activity. These proxies, while economic in form, are deeply social in consequence—shaping job quality, sectoral viability, and long-term inclusiveness. The dependent variable, youth employment, is thus positioned not as an isolated labour statistic but as an outcome of structural transitions that are simultaneously educational, technological, and ecological in nature.
3.3. Model Specification and Estimation Strategy
Thus, to achieve our goal, a dynamic panel-data regression model was adopted to test this, with all continuous regressors either first-differenced or logarithmically transformed so that they would take a stationary path and allow coefficients to be interpreted as elasticities. Key independent variables were the annual growth in real GDP per capita (Δ ln GDPpercapita), business enterprise R&D intensity (Δ BERD % GDPpercapita), and tertiary education growth (Δ ED_ATTAINMENT %). An interaction term Δ BERD × Δ ED_ATTAINMENT was included for possible saturation effects. Furthermore, sustainability covariates were energy productivity EUR/kg oil-eq. and total greenhouse gas emissions ln GHG, and two dummy variables controlled for the 2008–09 financial crisis and the COVID-19 shock of 2020–21. A lagged dependent variable EMP_{i,t − 1}and country fixed effects α_i were included to control for persistence and unobserved heterogeneity. Standard errors were clustered by country and year to allow for within-panel autocorrelation and heteroskedasticity.
All data were collected from Eurostat databases, hence the use of a balanced panel comprising 27 countries observed over 19 years, totalling 513 observations. GDP at market prices was recorded in purchasing power standard per capita and converted into natural logarithms. Business R&D and tertiary attainment were sourced as percentages of GDP and population, respectively. Energy productivity and greenhouse-gas emissions were obtained in standard units and logged where necessary to reduce skewness. Observations with missing values were excluded listwise, resulting in a fully balanced dataset. Ethical approval was not required as it involved the use of secondary, aggregate data.
The estimating equation is specified as follows:
EMPit = αi + ρ1EMPi,t−1 + β1ΔlnGDPit + β2ΔBERDit + β3ΔEDit + β4(ΔBERDit × ΔEDit) + β5ΔlnEPit + β6lnGHGit + γ1DummyFint + γ2DummyCovidt + β7Yeart + εi(1)
EMPit—is the employment rate of 25–34-year-olds in country i at time t;
αi—captures country-specific fixed effects;
ρ1—measures the persistence via the lagged employment rate EMPi,t−1;
ΔlnGDPit—is the first difference of the natural log of per capita GDP;
ΔBERDit—is the annual change in business–enterprise R&D as a percentage of GDP;
ΔlnEPit—is the annual change in tertiary education attainment (%);
ΔBERDit × ΔEDit—captures their interaction;
ΔlnEPit—is the change in energy productivity;
lnGHGit—is the natural log of total greenhouse gas emissions;
DummyFint—2008–09 financial crisis;
DummyCovidt—2020–21 pandemic shocks;
Yeart—is a linear time trend;
εi—is the idiosyncratic error term, with two-way clustering by country and year.
The preliminary model was estimated in EViews 12 (student edition) by pooled OLS. Before specifying the panel estimator, a diagnostic test that comprised Variance Inflation Factors (VIFs) for multicollinearity, the RESET test for functional-form misspecification, and further assessments of heteroskedasticity and autocorrelation formed the basis upon which to calibrate the final panel model. After which, Hausman and likelihood ratio tests confirmed that the fixed-effects specification is preferred, thereafter estimated with two-way clustered standard errors.
The main strengths of this approach lie in its ability to model persistence and interaction effects, but obviously, some limitations have to be acknowledged. Due to the constraints on data availability, the analysis was limited to 2005–2023; hence, any other earlier or recent dynamics that could be relevant have been left out. At the same time, using macro-level indicators may hide heterogeneity at the firm or sector level regarding the academia–industry–sustainability nexus. Although clustering corrects for certain dependencies, one cannot entirely rule out residual cross-sectional correlation. Future research can try to extend this time horizon, add micro-level data, and apply system-GMM estimators to further mitigate endogeneity concerns.
3.4. Limitations of the Model
Several caveats temper the generalisability of the reported findings. The empirical exercise relies on country-level series from Eurostat, so the analysis cannot reveal heterogeneity that may arise at the firm or sector level in respect of university–industry collaboration, digital uptake, or green retooling. Aggregate proxies such as growth in tertiary education and business research and development intensity capture broad structural tendencies, yet they do not reflect qualitative dimensions of innovation, for example, research excellence or process redesign. Moreover, the balanced panel begins in 2005 and ends in 2023; developments predating the major enlargement of the Union or emerging after the most recent data release fall outside the observation window. Although dummy variables isolate the global financial crisis and the pandemic period, other policy shocks or idiosyncratic events could still influence employment trajectories in ways the specification is unable to disentangle.
Methodological constraints may also leave residual endogeneity unresolved. The dynamic specification employs a lagged dependent term and two-way clustered standard errors, measures that address persistence and serial correlation, yet they do not provide the same degree of bias correction as a full system generalised method of moments estimator. Such an estimator was not feasible within the software environment available, as the Student Edition of EViews does not support system GMM routines, and project resources did not extend to alternative platforms. A reduced sample test using a single-step difference GMM suggested sign stability, but the instrument count remained high relative to the cross-section, undermining statistical diagnostics. Future research would benefit from longer time horizons, micro data on co-patenting and green occupations, and the application of software that accommodates system GMM or multi-level modelling in order to address these outstanding issues more comprehensively.
4. Results
Our analysis is based on the “trilemma” framework, which posits that youth employment outcomes in the EU-27 are essentially determined by the interplay of three core dimensions: academia (where human capital is formed through tertiary education), industry (business–enterprise R&D as innovation efforts), and sustainability (energy productivity and emissions performance). While many researchers treat these factors independently, our model undertakes an investigation into how R&D and educational growth affect employment simultaneously, with how this relationship is conditioned by the broader environmental context, thus providing a holistic view of the synergies and trade-offs underpinning sustainable labour market development.
4.1. Preliminary OLS Diagnostic Assessment and Model Calibration
The pooled OLS specification was used as a first diagnostic framework, required due to limitations of EViews 12 Student Edition, which does not allow for direct two-way clustering in panel estimators. The results shown in Table 1 are thus meant to aid in checking the adequacy of our model and set the exact calibration criteria for the upcoming panel analysis.
Therefore, pooled OLS acts only as an introductory step to ascertain that the chosen regressors, transformations, and interactions constitute a sound base for dynamic fixed-effects estimation. The condition of multicollinearity was initially checked using centred Variance Inflation Factors, which gave out a maximum VIF of 2.379—far below the conventional threshold of 10 and above the cut-off level. Therefore, this implies that no single predictor is found to be excessively overlapping with others, hence ensuring the stability and interpretability of coefficient estimates.
Further support for linear specification was also evidenced from the Ramsey RESET test for functional-form misspecification, which returned F(1,474) = 2.379 (p = 0.116), confirming that additional higher-order or interaction terms were not required at this stage.
Classical OLS assumptions relating to the disturbance term were next considered. The Breusch–Godfrey LM test (up to two lags) yielded F(2,473) = 13.179 (p < 0.001), hence first-order autocorrelation, whereas the Breusch–Pagan–Godfrey test for heteroskedasticity gave F(10,475) = 1.208 (p = 0.283), indicating that homoscedasticity cannot be rejected. Also, normality was rejected by the Jarque–Bera test (JB = 104.21; p < 0.001). The Durbin–Watson statistic of 1.5600 is within an acceptable range but does not fully preclude serial dependence in the errors.
The above diagnostic results attested to our panel-data approach. With no problematic multicollinearity and misspecification noted, it means that the variables and the interaction term were appropriately chosen; however, the heteroskedasticity and autocorrelation reported herein demand robust inference. Therefore, country fixed-effects estimation was applied with the inclusion of a lagged dependent variable to model persistence as well as two-way clustered standard errors (country and year) to adjust for within-panel heteroskedasticity along with cross-section dependence. It is this stringent tuning that ultimately makes the final panel model adequately strong and correctly specified so that credible inferences can be made about the influence of the relationship between academia and industry sustainability practices on youth employment rates.
4.2. Panel Analysis of the Academia–Industry–Sustainability Triad’s Impact on EU-27 Youth Employment
The dynamic fixed-effects specification presented in Table 2 captures nearly total variation in youth employment with an R2 of 0.9745 and an adjusted R2 of 0.9725. The coefficient on the lagged employment rate (0.8749, p < 0.001) confirms that past conditions strongly inform current outcomes and justifies the inclusion of a temporal dynamic. Such persistence implies that changes in labour market policy or economic conditions will be reflected gradually in observed employment rates rather than instantaneously.
Real GDP per capita growth is introduced as a control variable, and it shows a positive, very significant coefficient (18.9613, p < 0.001). In other words, this means that countries having stronger aggregate demand also experience higher increases in youth employment. This relationship is in line with the view that broad-based economic expansion generates opportunities in various sectors, which mainly benefit the younger workers.
Innovation spending—measured as changes in business–enterprise R&D (ΔBERD)—and growth in tertiary education attainment (ΔED) both have positive effects on employment. The coefficient values are 3.3863 with p = 0.0114 for innovation spending and 0.1559 with p = 0.0019 for growth in education, respectively. The evidence speaks to the support of labour market entry from both firm-level research activity and human-capital development. Also, the negative interaction term (−2.7456, p = 0.0135) reveals diminishing returns: when the supply of graduates is already rising rapidly, additional R&D yields smaller incremental employment gains; thus, policy measures should balance investments in education and innovation to maintain efficiency.
Energy productivity appears to be neutral (coefficient 0.0084, p = 0.9974), suggesting from a classical perspective that gains in output per unit of energy do not inherently raise labour demand, since such efficiency improvements typically reflect capital deepening in mature industries and yield cost savings that are reinvested or retained as profits rather than used to expand employment. On the other hand, the positive association with logged greenhouse gas emissions (1.4513, p = 0.0058) indicates that there is still employment intensity in the traditional carbon-intensive sectors, and it strengthens further the argument for a managed transition to preserve jobs while reducing emissions.
Crisis indicators show divergent patterns. The global financial crisis dummy (−0.3525, p = 0.5369) is negative and not significant, meaning that the adjustment paths of the member states were different and that the fixed effects absorb much of this heterogeneity. In contrast, the COVID-19 dummy (−1.5371, p < 0.001) is significant and sizeable, indicating a sharp contraction in youth employment in the pandemic years. Therein lies the contrast, as the nature of shocks, financial versus epidemiological, can have very distinct labour market footprints.
The Linear Year trend comes in positively at + 0.0879 percentage points (t = 2.10, p = 0.051). Methodologically, keeping this time variable soaks up any leftover common trend—beyond the separate shock dummies—making sure that our estimates for GDP, R&D, education and other factors are not influenced by unmodelled secular forces raising employment during the 2006–2023 period.
Two-way clustering of the standard errors allows for heteroskedasticity and within-panel correlation. High explanatory power, temporal dependence, and significant structural covariates confirm that our specification captures both the technical dynamics and the substantive drivers of youth employment in the EU-27. Therefore, this model serves as a good basis for evaluating policies to enhance early-career employment outcomes with stimulating aggregate demand, innovation and education investments, and energy transitions as complementary strategies.
Figure 3 shows the conditional marginal effect of business R&D intensity on employment rate, drawn against the growth in educational attainment. The shaded area between the lower (blue) and upper (orange) bands is a 95% confidence interval around the estimated marginal effect at every observed level of educational growth. In places where both bounds are below zero, it is statistically demonstrated that increased R&D spending results in less employment; where they straddle zero, the effect is not significant; and where both bounds are above zero, it has positive employment effects.
The confidence intervals narrow significantly around moderate rates of educational growth (approximately −5 to +5 percentage points), because more data are available in that range, and hence the estimation is more precise. At the extremes—very low or very high educational growth—the confidence bands start to diverge, and this implies increased uncertainty when extrapolating beyond the central mass of the sample. The heteroskedasticity in precision warns against reading too much into the extremes of the distribution, while at the same time, it underpins our main finding: that both the sign and magnitude of the R&D–employment relationship depend very strongly on contemporaneous human-capital development.
The graph highlights, from a policy perspective, the role of adjunct investment in education in eliciting employment gains from R&D. In countries where educational attainment is at a standstill or decreasing, R&D seems to either displace jobs or not create enough new opportunities; hence, the net effect on employment is negative. On the other hand, in countries where education marches forward with vigour, innovation more reliably translates into job creation—presumably by creating new high-skill positions or improving the productivity of existing workers. Therefore, a dual strategy of increasing both research intensity and skill levels is vital for sustainable employment growth.
5. Discussion
This research paper carried out an investigation to explore the extent to which academic innovation and industrial transformation contribute to sustaining employment across the EU-27 member states, within the broader framework of the academia–industry-sustainability nexus. In addition, the main purpose of this study was to capture both the direct effects of economic and educational drivers on employment, as well as the interaction effects that may emerge between them; therefore, the methodological section is based on econometric modelling through a dynamic panel data model with fixed effects and robust two-way clustered standard errors. Hence, this allows us to position the research within the policy debate on how to ensure inclusive and sustainable growth in the face of technological disruption, environmental pressures, and evolving labour market dynamics. Table 3 below presents the main key findings and policy recommendations.
The main findings provide substantial empirical support for the central hypothesis of this article. Firstly, the results indicate a strong and statistically significant positive association between GDP growth and employment rates (β = 18.96, p < 0.001) that suggests that macroeconomic expansion remains a fundamental determinant of labour market performance, while investments in R&D, proxied by BERD, also positively influence employment (β = 3.3863, p = 0.0114) and put emphasis on the productive role of industrial innovation in job creation, notably in economies that benefit from dynamic private sector engagement. Secondly, educational attainment, as a measure of academic development, has been found to significantly enhance employment outcomes (β = 0.1559, p = 0.0019), which reinforces the view that higher levels of human capital facilitate access to employment opportunities and support long-term market resilience.
One particularly noteworthy result emerges from the negative and statistically significant interaction term between BERD and education (β = −2.7456, p = 0.0135), which tends to indicate that the employment-enhancing effect of industrial R&D is weaker in countries with higher levels of educational attainment. This counterintuitive finding may reflect a shift in the nature of innovation, where the highly educated economies rely more on automation and advanced technologies, making innovation less labour-intensive and reducing the marginal employment gains from additional R&D spending. This result aligns with the scientific literature on the polarisation of labour markets in advanced economies and the risk of innovation-induced employment decoupling [26,27,28,29,30].
The multidimensional role of sustainability is also evident in our estimates. Energy productivity enters with a near-zero and statistically insignificant coefficient (β = 0.0084, p = 0.9974), implying that efficiency gains per se do not translate directly into higher employment—an outcome that accords with the classical notion that cost-saving innovations, particularly in capital-intensive sectors, may simply be retained as profits or invested in further automation rather than expanding the workforce. By contrast, the positive and highly significant effect of greenhouse-gas emissions (β = 1.4513, p = 0.0058) indicates that, despite the EU’s green agenda, a substantial share of job creation remains anchored in polluting industries. From a theoretical standpoint, this pattern reflects a transitional phase in which growth and employment continue to rely on carbon-intensive activities even as cleaner technologies advance, underscoring the need for targeted policies that couple environmental improvements with support for labour-intensive green sectors. In contrast, the positive coefficient on greenhouse gas emissions (β = 1.4513, p = 0.0058) might indicate that some employment growth still relies on polluting industries, thus revealing the transitional nature of the EU’s green transformation. Hence, these results further place emphasis on the structural tension between short-term employment gains and long-term environmental sustainability. With regards to the control variables, the model confirms the strong persistence of employment dynamics through the lagged dependent variable (β = 0.8749, p < 0.001). In addition, the financial crisis dummy (β = −0.3525, p = 0.5369) was found to be statistically insignificant, whereas the COVID-19 dummy (β = −1.5371, p < 0.001) had a clear and negative effect, thereby confirming the disruptive impact of the pandemic on labour markets across Europe.
On one side, the present study’s findings align with and extend scholarly efforts that seek to carry out investigations on the nexus between academic advancement, digital transformation, and labour market sustainability. For example, a significant point of convergence is observed with Molina-Espinosa et al. [26] who emphasise the critical role of academic performance in cultivating reasoning skills pertinent to the complexity and digital transformation. Likewise, their conceptualisation of education as a driver of meta-competencies resonates strongly with the present study’s conclusion that academic attainment functions not merely as an endpoint of the educational process but rather as a dynamic input into broader innovation ecosystems. Therefore, both studies support the proposition that educational systems must transcend traditional content delivery and embrace competency-based frameworks that are responsive to digital and ecological imperatives.
Furthermore, Raveica et al. [27] carried out an exploration of industrial engineering students’ perceptions regarding their training and employability in a sustainable economy, which revealed that students increasingly value training experiences that align with the digitalisation of industry and environmental awareness. This observation complements the argument advanced herein that tertiary education attainment must be interpreted not only quantitatively but also qualitatively, through the lens of emerging labour market structure relevance. Similarly, Silvestru et al. [28] analyse the implications of sustainability for employability within the construction industry, thus further confirming that sector-specific dynamics must inform policy and educational strategies. Therefore, the authors support the view that the translation of educational attainment into sustainable employment is mediated by the nature of the industry and the maturity of its green practices, a proposition echoed by the current study’s insistence on contextual nuance.
Additionally, the dialectics of digitalisation are further investigated by Szabó-Szentgróti et al. [29], who reinforce the notion that this phenomenon is not unidirectional in its employment effects but rather dialectical due to its negative consequences, like job displacement. In their work, the authors carry out an exploration of the macroeconomic implications and automation by verifying elements of Keynes’ prediction regarding technological unemployment while simultaneously identifying novel occupational niches born out of Industry 4.0. In a similar vein, Wang et al. [30] adopt a global perspective to analyse how megatrends, including the digital economy and pandemic-induced disruptions, have catalysed transformations in career management across Asian economies. Their conclusions contend that the challenges and imperatives of digital transformation, employability, and sustainable development are not regionally confined but manifest with considerable consistency across diverse economic contexts. Consequently, these studies substantiate and reinforce the central contention of this research, stating that employment sustainability in the European Union is increasingly determined by the capacity of education systems and innovation policies to respond coherently to digital and ecological transformations.
Notwithstanding the robustness of the analytical framework and the empirical validity of the results, several limitations of the present study must also be acknowledged. First, the operationalisation of key constructs such as innovation and academic progress through aggregate proxies like BERD and tertiary education attainment, while methodologically convenient, might obscure important qualitative dimensions. Consequently, the influence of other variables, like the nature and orientation of research activities, the degree of interdisciplinarity, or the actual alignment between educational curricula and labour market demands, is not fully captured, yet future research could address this limitation by incorporating more granular indicators. Second, the present analysis adopts a macroeconomic perspective and approaches the EU member states as relatively homogeneous entities, which is considered to be well suited for identifying structural trends. However, European labour markets are also characterised by institutional and sectoral heterogeneity, one constraint that could be overcome by subsequent studies that consider multi-level modelling or cluster analysis that accounts for regional disparities, sectoral composition, and differences in innovation governance. Third, in spite of the reverse causality mitigated through dynamic modelling, the potential for residual endogeneity cannot be entirely excluded, especially in relation to policy interventions or latent institutional factors. Hence, a promising direction for future inquiry could be the adoption of quasi-experimental designs or instrumental variable approaches that enable more precise causal inference. Last but not least, the interpretative depth of these studies could be enriched by complementing quantitative analyses with qualitative case studies, notably in tracing how academic and industrial actors co-produce employment outcomes within specific innovation ecosystems.
Despite these limitations, our article makes a distinctive contribution to the scholarly discourse on sustainable development and labour market transformation due to the fact that it integrates empirically the domains of economic performance, academic attainment, industrial innovation, and environmental productivity, thereby advancing a multidimensional understanding of employment dynamics across EU member states. Thus, this research responds to the pressing need for evidence-based insights capable of informing policy at the intersection of human capital formation, technological advancement, and ecological responsibility. Moreover, the results support the view that employment sustainability is not merely a function of growth or education in isolation but rather the product of a complex interplay between academia, industry, and the imperative of environmental transition. In conclusion, this study offers a valuable analytical lens through which future strategies for inclusive and future-oriented employment policies may be designed, implemented, and evaluated.
6. Conclusions
This paper’s main purpose was to provide an explanation of the extent to which tertiary education attainment, environmental productivity, and innovation-related R&D expenditure contribute to influencing employment sustainability across the European Union, with a particular focus on the period spanning from 2005 to 2023. Anchored in the broader context of the digital and green transitions, this study aimed to provide clarification on how human capital development, environmental policy performance, and investment in research and development coalesce to influence labour market resilience and adaptability. In addition, the analysis was motivated by an imperative to provide a clear image of the structural factors influencing employability in a rapidly evolving economic landscape, where knowledge intensity, sustainability imperatives, and technological innovation increasingly dictate patterns of inclusion or exclusion.
Furthermore, the empirical results tend to suggest a coherent and statistically robust connection between education levels, environmental productivity, and employment sustainability, notably when analysed through a dynamic panel data framework that accounts for cross-sectional heterogeneity. Thus, the significance of tertiary education attainment as a driver of sustainable employment places emphasis on the need for continued investment in higher education that is aligned with digital competencies and green awareness. Simultaneously, the positive impact of R&D expenditure further emphasises the strategic role of innovation ecosystems in creating adaptive employment pathways capable of absorbing technological shocks. Taken together, these findings indicate that a coherent and forward-looking policy architecture that integrates education, innovation, and environmental performance is indispensable for safeguarding employment sustainability. On the other hand, fostering a resilient and inclusive labour market in the European Union greatly depends on the strategic coordination of higher education reform, innovation incentives, and ecological transition mechanisms, which must be seen as synergistic pillars of a sustainable socio-economic model.
Overall, after revisiting and responding to the research question formulated at the outset, this research paper confirms that the pursuit of sustainable employment is neither an incidental by-product nor an automatic outcome of economic growth; rather, it is the result of deliberate systemic investment in human capital, environmentally productive processes, and innovation capacity. Consequently, this paper enriches the existing academic dialogue on the determinants of employability in the 21st century and furnishes policymakers with actionable insights into how to design integrative strategies that simultaneously advance educational equity, ecological integrity, and economic resilience.
Conceptualization, A.H.; Methodology, M.C.; Software, M.C.; Validation, S.-E.I.; Formal analysis, A.H.; Investigation, M.C. and G.-T.B.; Resources, S.-E.I.; Data curation, M.C.; Writing—original draft, A.C.; Writing—review & editing, A.C.; Visualization, G.-T.B.; Supervision, A.H.; Project administration, A.C.; Funding acquisition, A.H. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
The raw data supporting the conclusions of this article will be made available by the authors on request.
The authors declare no conflicts of interest.
The following abbreviations are used in this manuscript:
| BERD | Business enterprise R&D intensity |
| EU | European Union |
| GDP | Gross Domestic Product |
| GMM | Generalised Method of Moments |
| JB | Jarque-Bera |
| LM | Lagrange Multiplier |
| OLS | Only Least Squares |
| R&D | Research & Development |
| VIF | Variance Inflation Factor |
Footnotes
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Figure 1 Conceptual framework of the employment trilemma employed in this study.
Figure 2 Research design framework—linking innovation, transformation, and youth employment.
Figure 3 Marginal effect of business R&D on employment rate across educational attainment levels.
Diagnostic tests for pooled OLS specification 1.
| Test/Check | Statistic | p-Value | Findings |
|---|---|---|---|
| Multicollinearity | Max VIF = 2.379 | - | Multicollinearity is solved (all VIFs <10) |
| Functional form | F(1,474) = 2.475 | 0.116 | No misspecification |
| Serial correlation | F(2,473) = 13.178 | <0.001 | First-order autocorrelation present |
| Normality | JB = 102.653 | <0.001 | Residuals non-normal |
| Heteroskedasticity | F(10,475) = 1.208 | 0.283 | Heteroskedasticity is not present |
1 Diagnostic results from centred VIF, Ramsey RESET, Breusch–Godfrey LM (2 lags), Jarque–Bera and Breusch–Pagan–Godfrey tests provided the empirical foundation for adopting a fixed-effects panel estimator with two-way clustered standard errors.
Fixed-effects panel estimation of youth employment determinants (EU-27, 2005–2023).
| Variable | Coefficient | Std. Error | t-Statistic | p-Value |
|---|---|---|---|---|
| Constant (C) | −184.1624 | 85.7027 | −2.1489 | 0.0463 |
| Δ log GDP | 18.9613 | 3.4278 | 5.5316 | 0.0000 |
| Δ BERD | 3.3863 | 1.1936 | 2.8370 | 0.0114 |
| Δ ED Attainment | 0.1559 | 0.0424 | 3.6780 | 0.0019 |
| Δ BERD × Δ ED Attainment | −2.7456 | 0.9956 | −2.7576 | 0.0135 |
| Δ log EP | 0.0084 | 2.5417 | 0.0033 | 0.9974 |
| log GHG | 1.4513 | 0.4603 | 3.1526 | 0.0058 |
| Dummy (Financial Shock) | −0.3525 | 0.5594 | −0.6302 | 0.5369 |
| Dummy (COVID) | −1.5371 | 0.2823 | −5.4455 | 0.0000 |
| Employment Lag (t – 1) | 0.8749 | 0.0283 | 30.9617 | 0.0000 |
| Year | 0.0879 | 0.0418 | 2.1013 | 0.0508 |
Model fit: R2 = 0.9745; Adj R2 = 0.9725; F = 478.032 (p < 0.001), and DW = 1.56.
Key findings and policy implications 3.
| Key Findings | Policy Recommendations |
|---|---|
| Expansion of tertiary education attainment supports higher youth employment but shows diminishing returns when paired with R&D. | Synchronise R&D incentives with expanded tertiary education and vocational training to build absorptive capacity. |
| Increases in business–enterprise R&D intensity drive employment gains, conditional on workforce skills. | Develop integrated innovation-and-education programmes that link research grants to upskilling initiatives. |
| Energy–productivity improvements alone do not affect aggregate employment. | Complement efficiency-enhancing regulations with labour market support (e.g., retraining grants) to capture job potential. |
| The positive link between GHG emissions and employment highlights reliance on carbon-intensive sectors. | Implement just-transition policies combining green technology support with targeted worker retraining and subsidies. |
| Employment outcomes exhibit strong persistence and a secular upward drift. | Design multi-year reform frameworks—such as continuous upskilling and stable R&D funding—to reflect gradual adjustment. |
| Crisis impacts differ by shock type (uniform in pandemics; heterogeneous in financial crises). | Tailor relief measures: broad-based support for systemic shocks and sector-specific aid for financial sector downturns. |
3 Based on dynamic panel estimates with fixed effects and two-way clustered standard errors, integrating human-capital, innovation and environmental indicators to explain youth employment variation.
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Details
; Silvia-Elena, Iacob 2 ; Constantin, Alexandra 2
; Cetulean Maxim 3
; Georgiana-Tatiana, Bondac 4
1 Department of Economics and Economic Policies, Bucharest University of Economic Studies, 010374 Bucharest, Romania; [email protected]
2 Department of Economic Doctrines and Communication, Bucharest University of Economic Studies, 010374 Bucharest, Romania; [email protected]
3 Doctoral School of Economics I, Bucharest University of Economic Studies, 010374 Bucharest, Romania; [email protected]
4 Doctoral School of Economic and Humanities, “Valahia” University of Târgoviște, 130004 Târgoviște, Romania; [email protected]