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Mapping Montenegro’s potential in the context of Smart Specialisation
Get PDFThe Smart Specialisation Strategy (S3) is a place-based economic agenda that Montenegro, as the first non-EU country to adopt a strategy based on this framework, is now updating for the 2026–2031 period. This new iteration elevates S3 to a national ‘umbrella’ strategy, utilizing comprehensive quantitative and qualitative mapping to identify the country’s economic, scientific, and innovative strengths. The resulting report serves as an analytical foundation for the upcoming Entrepreneurial Discovery Process (EDP), where stakeholders collaborate to finalize Montenegro’s strategic priority domains. The analysis identifies five preliminary priority areas for Montenegro’s 2026–2031 S3 strategy: Construction, Energy and Sustainable Environment, Sustainable Agriculture and Food, Digital Innovation and Transformation, and Innovative and Sustainable Tourism. While sectors like Construction and Energy are highlighted for their roles in infrastructure and green transitions, the ICT and Tourism sectors stand out as high-growth pillars, contributing significantly to GDP and export potential.JRC.B.3 - Territorial Developmen
Integrated governance in data ecosystems: A conceptual framework consolidating collaborative and data governance
No full textThe potential benefits deriving from inter-organizational data sharing have increased over time, leading to an intensified interest in data ecosystems. The governance of these endeavors depends on both collaborative and data governance dimensions. However, previous research has often treated these dimensions separately, creating silos that hinder the capacity to deliver value considering their socio-technical nature. Addressing this gap, this study investigates the intertwined relationship between these two dimensions within data ecosystems. It does so by questioning which existing and most relevant relationships exist between them, as well as the nature of these relationships. To this end, we adopt a multiple case study approach, analyzing five data ecosystems. The research led to the development of a conceptual framework for Integrated Governance, highlighting the need for a holistic socio-technical approach that addresses collaborative and data governance dimensions as intertwined. The framework unveils 24 core relationships between these dimensions in data ecosystems and provides insights on the nature of the relationships, distinguishing among causal, explanatory, concurrent, chronological, and overlapping ones. This work introduces a new perspective in the academic discourse on data sharing providing actionable insights for practitioners and enabling them to design and manage data ecosystems more effectively.JRC.T.4 - Digital and Data Sovereignt
Transforming Cities
Get PDFThis report examines barriers and enablers shaping the role of cities in the EU green transition pathway. It presents the outcomes of an international expert workshop organised by the Joint Research Centre (JRC) of the European Commission in collaboration with Politecnico di Torino, Italy. The workshop is part of a series of JRC exploratory research activities applying innovative methods to co-create shared visions for system transitions across the EU food, urban, and green innovation domains.
During the workshop, experts from academia, architectural and landscape firms, policymaking institutions, major urban networks, living labs, the European Committee of the Regions, and non-governmental organisations identified enablers and local good practices to strengthen the implementation of selected European Green Deal targets at the urban level and to address structural challenges hampering transformative change.
Seven enabling conditions emerged as critical for advancing urban green transitions: strengthened and systematic community and citizen engagement; upgrading urban infrastructure and uptake of nature-based solutions; innovative governance and policymaking; sustainability education and skills; coupled energy and digital transitions; adequate financial resources and public-private cooperation; and transparent communication and information sharing.
A central element of the workshop was ShapeEUrbe, a science-based board game specifically designed for this event. Through the game, participants explored policy gaps, good practices, barriers and enablers for achieving the European Green Deal targets and envisioning urban futures towards the EU’s 2050 climate neutrality ambition.JRC.D.3 - Sustainable Supply Chains and Bioeconom
Escaping the Inactivity Trap? The Work Incentive of the Spanish Minimum Income
No full textThe Spanish Minimum Income scheme, introduced in 2020, offers beneficiaries a unique national guaranteed income as a last-resort benefit. However, the scheme’s design featured a lack of work incentives for low earners, potentially leading to inactivity traps. To address this flaw the Spanish government introduced an earnings disregard in 2022, enabling beneficiaries to keep all or part of the benefit when their earnings increase up to a certain limit. This paper provides an ex ante assessment of this reform, looking into its expected fiscal, distributional and labour market effects using the tax–benefit microsimulation model EUROMOD, and the behavioural labour supply model EUROLAB. Our results show that the reform has the potential to incentivise work for very low earners, particularly lone parents, mainly by promoting part-time employment. The reform and its subsequent employment effects are also expected to slightly reduce inequality and poverty. While this is a step in the right direction, we discuss some avenues for improvement.JRC.B.2 - Fiscal Policy Analysi
Identification of essential variables for the estimation of energy demand in buildings at European scale
No full textAccurate European scale building energy modelling remains challenging due to fragmented data, yet it is essential for assessing policy scenarios. This study proposes a data-driven methodology to identify a minimal set of essential variables for a reduced-form model capable of accurately predicting energy demand across European buildings. Using Random Forest machine learning applied to two complementary datasets: (1) a cross-country database of building typologies across the Member States of the European Union (EU), and (2) the French Energy Performance Certificate (EPC) database—the analysis identifies five key variables that reduce mean absolute percentage error by 10 percentage points compared to using all 18 original available attributes. These variables are: living floor area, height, construction year, climatic conditions, and building functionality, while construction materials and other architectural features show limited predictive relevance. The study fills a gap in the literature by rigorously establishing essential variables required for EU-scale energy modelling, complementing previous work that either lacks cross-country generalization or focuses on single-country assessments. The main novelty lies in demonstrating that competitive predictive accuracy can be still achieved using standardized, widely available features, challenging the assumption that detailed physical data are required for robust models. This approach prioritizes scalability and cost-effectiveness, enabling wider development across Europe towards the creation of a detailed EU Digital Building Stock Model for energy-related purposes. As key variables can be derived from Earth Observation, cadastral records, and machine-learning-based inference, the findings support harmonised, building-level energy estimation and facilitate applications such as identifying worst-performing buildings, prioritizing renovation actions, and informing decarbonization strategies.JRC.C.2 - Energy Efficiency and Renewable
Assessing the impact of Energy Efficiency on the EU Energy Consumption in 2010-2023
Get PDFThis report examines the determinants of changes in primary and final energy consumption at EU27 and Member State levels over the period 2010 to 2023 to track and understand the progress towards 2030 energy efficiency targets and beyond. Energy consumption trends are driven by several factors beyond energy efficiency improvements, which can have a profound effect in the aggregate energy use, irrespective of the impact of energy efficiency policies and measures. To understand the latest energy consumption trends in the EU, the Logarithmic-Mean Divisia Index
(LMDI) approach, a widely used Index Decomposition Analysis (IDA) method, was applied to study both aggregated and sectoral energy consumption changes at EU and Member State levels over the examined period and quantify the impact of factors such as economic activity, demographics, productivity, lifestyle and weather changes. The results suggest significant energy efficiency gains from 2010 to 2023, without which the progress achieved towards 2030 EU energy efficiency targets would have been difficult to attain. However, any analysis for the recent years should be considered with caution as they have been significantly influenced by exceptional external factors. In 2020, the COVID-19 pandemic led to a drop in energy consumption, followed by a rebound effect once the restrictions were raised. Starting in 2022, the Russia’s war of aggression against Ukraine (was preceded from and) resulted in an increase in energy prices (especially for gas) with strong interventions by the EU and Member States to limit energy consumption. With the aim to investigate the evolution of the determinant factors of energy consumption, an analysis of the energy consumption projections for selected sectors (i.e., industry, services, agriculture and residential) up to 2050 at the European level has been carried out.JRC.C.2 - Energy Efficiency and Renewable
JRC-IDEES-2023: the Integrated Database of the European Energy System
Get PDFThe Joint Research Centre's Integrated Database of the European Energy System (JRC-IDEES) incorporates in a single database a rich set of information allowing for highly granular analyses of the dynamics of the European energy system, so as to better understand the past and create a robust basis for future policy assessments. JRC-IDEES provides a consistent set of disaggregated energy-economy-emissions data for each Member State of the European Union, covering all sectors of the energy system for the 2000-2023 period. This data complies with Eurostat energy balances while providing a plausible decomposition of energy consumption into specific processes and end uses. In each sector, JRC-IDEES uses a vintage-specific approach to quantify the characteristics of the energy-using equipment in operation, along with the average operation of the equipment stock. It accordingly identifies different drivers and provides insights on their role by sector while accounting for structural differences across countries. JRC-IDEES therefore supports several key applications for energy modelling, research, and policy analysis, such as parameterizing energy models and assessing past and prospective policies.
JRC-IDEES is freely accessible to the general public since 2018 and can be downloaded through the JRC Data Catalogue. This report documents the 2025 release (JRC-IDEES-2023), which incorporates several new data sources and methodological improvements while extending the time coverage of the database until 2023. As such, the report is a revision of the previous JRC-IDEES-2021 technical report and describes key changes where relevant. The report also includes general minor revisions to clarify assumptions and data sources.JRC.C.6 - Economics of Climate Change, Energy and Transpor
Influences of outliers on performance of geographically weighted random forest for modelling cadmium concentrations in topsoil of the northern part of Ireland
No full textCadmium (Cd) is a toxic element ubiquitously distributed in the environment. Numerous models have been employed to predict soil Cd concentrations, among which local models can better capture spatial patterns and yield more accurate predictions than global models. However, their sensitivity to outliers could lead to substantial local errors. In this study, we aim to assess and reduce the outlier effect in local modelling based on topsoil Cd in Ireland from Tellus project and 12 influential factors. Geographically weighted random forest (GWRF) was integrated with outlier detection tools Local Moran’s I (GWRF-LISA) and Z-score normalization (GWRF-Z). The local models were compared against traditional global random forest (RF). Results showed that outliers could cause radial clusters, leading to spatially autocorrelated residuals. This effect strengthens with increasing bandwidth in local models. Z-score can effectively reduce outlier effect by adaptive removal of outliers. Among the four models, GWRF-Z produced the most accurate predictions, but its interpretability was limited by small bandwidths. SHAP values of RF revealed that precipitation, pH, and soil type were dominant factors in about 60 % of the study area, indicating the significant role of pedoclimatic processes in Cd distribution in Ireland. This study has clarified the influence of outliers in local modelling and highlighted the effectiveness of Z-score in reducing outlier effect. The proposed approach showed potential applications in broader regions. These findings provide a scientific basis for spatially targeted interventions and support local decision-making.JRC.D.1 - Forests and Bio-Econom
Export-import assessment of Tanzania's produce market post-African Continental Free Trade Area agreements: The case of fruits and vegetables
No full textForming the African Continental Free Trade Area (AfCFTA) is a key step towards Africa's economic integration, promoting intra-African trade and sustainable growth. This study employs the Dynamic Equilibrium Model for Economic Development, Resources and Agriculture (DEMETRA) developed by the European Commission's Joint Research Centre. DEMETRA is calibrated using Tanzania's 2015 Social Accounting Matrix (SAM) to assess the impact of the AfCFTA on the country's fruits and vegetables subsectors. Throughout the simulation period, fruit imports in Tanzania are projected to increase by 65.7\%, reaching 25.2 billion Tanzanian shillings (Tshs) in value, while exports, particularly under the revenue enhancement (REV) schedule, are expected to grow to 128.9 billion Tshs in value. Vegetable imports are expected to increase in value by 5.3\%, while exports are projected to decline by 1.5\%. The study encourages authorities to strategically utilize the revenue enhancement (REV) schedule to optimize export profits and recommends enhancing agricultural infrastructure and resources to support sustainable export growth.JRC.D.4 - Economics of the Food Syste
Which traits drive consumer preferences for gene‑edited foods in Spain
No full textThis study examines consumer preferences for the potential benefits of CRISPR technology using a best–worst scaling (BWS) approach within an online survey of a representative Spanish sample. The BWS discrete choice experiment focuses on seven key environmental and health-related benefits of CRISPR, using tomatoes as a case study. The selected benefits are derived from science-based information and align with the EU regulatory context, following the European Commission’s 2023 proposal on gene-editing technologies. Estimates from a random parameter logit (RPL) model indicate that pesticide reduction is the most highly valued benefit, followed by water saving and health improvement, thereby highlighting the combined influence of environmental and personal benefits on consumer acceptance of genetically engineered food. The significant standard deviations in the RPL estimates reveal substantial heterogeneity in preferences, which is further examined by identifying two distinct consumer segments. While both segments strongly prioritise pesticide reduction, one is primarily motivated by environmental sustainability outcomes, whereas the other places greater emphasis on health and sensory quality improvements. These findings underscore the need for targeted communication strategies to address distinct consumer concerns, rather than a uniform approach.JRC.D.4 - Economics of Food System