Cristiana Tudor

Corporate renewable‐energy transition is often assessed through climate commitments, ESG scores, or carbon indicators, although these measures may not reveal firms' capacity to translate formal policies into operational change. Using a newly assembled point‐in‐time panel of European listed companies drawn from the STOXX universe over 2015–2025, this study constructs a holistic environmental‐management architecture score comprising resource/nature and climate/energy management pillars. The common estimation samples contain 1161 firm‐year observations from 264 firms for the 1‐year horizon and 869 observations from 215 firms for the 2‐year horizon. Firm and fiscal‐year fixed‐effects models relate environmental‐management architecture in fiscal year to subsequent changes in renewable‐energy use over and , complemented by pillar‐level and component‐level decompositions, alternative fixed‐effects structures, and sector exclusions. The holistic architecture score is positive but imprecisely estimated. Resource/nature management is positively associated with subsequent renewable‐energy improvement at both horizons, whereas climate/energy formalization is not. Water‐efficiency policy is marginally significant at the 1‐year horizon and statistically significant at the 2‐year horizon. The findings show that formal environmental policies do not carry uniform information about operational transition and highlight the value of examining policy domains and implementation‐related organizational routines separately.

This study examines how public debt conditions inflation pass-through to observed interest-rate conditions in the European Union. Using EU country-year data for 1999–2024 and dynamic estimations for 2000–2024, we estimate reduced-form panel models in which inflation pass-through varies smoothly with lagged public debt through natural cubic splines. In the main short-rate specifications, pass-through is strongest at very low debt levels and weaker across the central and upper parts of the debt distribution, without a uniformly monotonic decline. The pattern is concentrated at impact and short annual horizons and remains visible in clean short-rate and non-euro samples. Euro-area sovereign-spread specifications do not reproduce the same baseline attenuation profile. By showing that fiscal conditions shape the strength and timing of inflation pass-through across monetary regimes, the study advances understanding of heterogeneous interest-rate adjustment in a fiscally fragmented European Union.

Europe's twin green and digital transition seeks to align sustainability with digitalization, yet it remains unclear whether progress in twin transition‐relevant SDGs coincides with changes in economic freedom dimensions. Using an EU‐27 panel from 2000 to 2024, we examine the link between the Heritage Index of Economic Freedom and a specific SDG Strategic Priorities Index (SDG_SPI), which aggregates SDGs 4, 7, 8, 9, 13, and 17. The empirical design uses two‐way fixed effects with lagged regressors and Driscoll–Kraay standard errors to estimate within‐country associations while accounting for country heterogeneity, common time shocks, cross‐sectional dependence, and serial correlation. The results indicate an uneven institutional footprint. SDG_SPI is not robustly associated with the overall economic freedom score, Rule of Law, or Regulatory Efficiency. Its most consistent association concerns Government Size. A one‐point increase in SDG_SPI is linked to a Government Size score lower by roughly 1.4 points, which reflects a larger fiscal footprint under Heritage coding rather than weaker institutional quality. Evidence for Market Openness is positive in baseline models but becomes fragile once outcome persistence is modeled. In otherwise comparable specifications, the broader SDG index displays a more diffuse institutional association across pillars. The findings highlight the value of decomposing economic freedom when assessing the institutional footprint of the twin transition.

This paper examines whether the output elasticity of energy varies across emissions states and whether aggregate emissions or emissions intensity provides the more informative conditioning variable. A fixed-effects panel threshold model is estimated for 110 countries over 2006–2022. Logged total greenhouse-gas emissions are first used to select regimes endogenously. Under this benchmark specification, energy raises output in both regimes, and the point elasticity is larger in the high-emissions state, but the cross-regime difference is not statistically distinct under inference robust to cross-sectional dependence, while bootstrap evidence provides only limited support for a global threshold based on aggregate emissions. The empirical picture is sharper when emissions intensity is used as the conditioning state. In that case, the energy–output elasticity is lower in high-intensity regimes, and the contrast becomes more visible in interaction-based specifications and after 2015. Source decomposition shows that both renewable and non-renewable energy are less productive in high-intensity regimes, with the attenuation especially clear for non-renewables and with renewables turning negative in the high-intensity state. Overall, aggregate emissions mainly sort countries by scale, whereas emissions intensity more clearly captures economically meaningful heterogeneity in the productivity of energy.

The pursuit of sustainable development in higher education institutions (HEIs) demands leadership capable of managing complex, interconnected challenges—from curricular integration to carbon reduction. While HEIs are pivotal in driving sustainability transitions, leaders often struggle to balance stakeholder demands, financial constraints, and the demands of systemic change. Current research lacks clarity on effective strategies for navigating these dynamics, leaving a gap in understanding how leadership can advance sustainability amid uncertainty. This study identifies leadership approaches that balance competing priorities and foster systemic innovation in HEIs. It conducted a systematic review of transformational, distributed, and sustainability leadership theories, complemented by qualitative case studies of sustainability‐leading institutions. Data from policy documents, sustainability reports, and leadership observations reveal that effective leadership requires strategic vision, collaborative governance, and network engagement. Key success factors include empowering grassroots initiatives, embedding sustainability into institutional identity, and applying adaptive practices. The paper proposes a “complexity‐aware” leadership framework that offers practical guidance for administrators and policymakers. It underscores how leadership that embraces uncertainty, builds resilience, and aligns academic missions with sustainability imperatives can drive transformative change.

This paper examines whether broad enterprise AI adoption in Europe is best understood as an isolated technology decision or as the outcome of a wider bundle of digital capabilities. Using harmonized Eurostat data for European enterprises, the analysis builds a repeated cross-section at the country–size-class–year level and models high AI adoption with a combination of random forest and elastic-net estimation. The dependent variable captures enterprises using at least one AI technology, while the explanatory set focuses on cloud adoption, cloud CRM, cloud ERP, cloud database hosting, cloud security, cloud software use, e-sales intensity, and enterprise size. The findings reveal a stable predictive structure and consistent classification performance across specifications. Across models, cloud CRM and e-sales emerge as the strongest predictors of high AI adoption, followed by general cloud use and selected data-related cloud capabilities. This ordering remains largely stable in threshold-sensitivity checks based on alternative definitions of high adoption. The pattern also remains visible when country controls are removed, which suggests that the result is not merely a reflection of national heterogeneity. The paper contributes by shifting attention from broad claims about “digital readiness” to a narrower and more operational notion of capability complementarity: AI uptake tends to cluster where firms already possess customer-facing, cloud-based, and commercially digital infrastructures. In that sense, the paper offers a transparent, reproducible, and policy-relevant account of the digital foundations of enterprise AI adoption in Europe.

The European Union aims to cut greenhouse gas emissions by 55% by 2030 and reach climate neutrality by 2050, targets that depend on expanding renewable generation in the European energy system. While photovoltaic (PV) capacity has grown quickly in several member states, others remain far behind. This paper frames that divergence as a systems planning problem: installed MW expands through diffusion-like dynamics, but the conversion of investment into energizable capacity is filtered by grid-integration constraints and institutional throughput. The study develops a macro-level framework for systems-level assessment and decision support to guide PV capacity planning and budget allocation using official 2012–2022 data for 22 EU countries. We combine (i) unsupervised clustering of standardized national deployment trajectories, (ii) bounded logistic fits interpreted as an operational diffusion-with-saturation representation that yield comparable growth parameters and maturity years (80–90% of the estimated ceiling), and (iii) a proportional reallocation scenario for countries below 5 GW in 2022. Three clusters emerge—steady growth, early plateau, and atypical paths—and an analytically tractable maturity indicator integrates capacity, rate, and timing in a single measure. In a 10 GW reallocation scenario, average progress toward the 5 GW benchmark rises from 9.8% to 23.1%, closing about 14.8% of the aggregate shortfall. The allocation experiment reveals a clear asymmetry: systems with an existing installed base convert additional MW into benchmark progress more efficiently than very low-baseline systems, where binding constraints are more likely to sit in permitting, interconnection queues, and hosting capacity rather than in finance alone. Turning these allocations into usable capacity depends on timely interconnection and power-electronics integration and on grid-enablement constraints such as interconnection readiness, inverter compliance, and local hosting capacity in high-penetration areas. The contribution is a transparent, updateable decision-support pipeline that links observed trajectory regimes to a maturity “clock” and an auditable allocation baseline, making the trade-off between closing capacity gaps and respecting feasibility filters explicit in an EU system with heterogeneous national subsystems. The proposed approach links macro-level maturity clusters to operational feasibility signals in the grid integration layer, showing that modeling-based allocation can improve system progress but cannot substitute grid-enablement measures, highlighting the importance of regional coordination in the EU energy system under heterogeneous national trajectories.

Purpose: The blue economy includes a variety of economic activities and provides a remarkable contribution to sustainable development. This paper aims to assess the financial performance of diverse sustainable economy activities and evaluate whether blue firms based on natural resources, mainly water behave compared with clean energy firms and in contrast to a broad-market benchmark. Design/methodology/approach: The study estimates per-exchange traded fund (ETF) time-series Capital Asset Pricing Model (CAPM)/ Fama-French Three-Factor (FF3)/ Fama-French Five-Factor (FF5) models on daily excess returns from July 2008 to November 2024. Coefficients are obtained by ordinary least squares (OLS) with Newey–West/HAC standard errors to address heteroskedasticity and serial correlation. As a robustness check, the study also runs a pooled regression with factor×ETF interactions to test cross-ETF equality of factor loadings (Wald tests). This design provides a robust empirical assessment of the risk–return drivers for blue economy and clean energy ETFs within standard asset-pricing theory. Findings: The results show how blue economy water-related investments are more profitable, less volatile and follow more conservative investment policies than renewable energy investments. Findings also suggest a preference for value-driven firms within water-related industries, while clean energy firms are often growth-oriented. This confirms that the blue economy firms should be preferred to clean energy firms, within the investment portfolios following a binomial risk-return strategies. Originality/value: Blue economy stocks are increasingly gathering the interest of investors which follow a risk-return strategy but also include sustainable development principles in their investment decisions. While some studies have analyzed the impact of green and other sustainable factors in the financial performance and investors preferences, to the best of the authors’ knowledge, this study is the only one or among the very few that conduct this analysis within blue economy stocks.