European Union countries’ recovery and resilience plans

Publishing date
20 February 2023
Graph dataset

Last updated: 20 February 2023

Click on this link to read about what has been added in this update. 


The Recovery and Resilience Facility (RRF) is the largest component of Next Generation EU (NGEU), the European Union’s landmark instrument for recovery from the coronavirus pandemic. The RRF will provide grants amounting to at most €312.5 billion at 2018 prices, or €338 billion at current prices, and loans amounting to at most €360 billion at 2018 prices or €390 billion at current prices. EU countries have to submit national recovery and resilience plans that describe the reforms and public investment projects they plan to implement with the support of the RRF.

EU countries should have officially submitted their recovery and resilience plans “as a rule” by 30 April 2021 (see paragraph 38 of the Preamble and Article 18(3) of the RRF Regulation). However, this deadline is flexible and the Commission has argued that countries can submit their plans up to mid-2022. Thirteen countries submitted their plans by the 30 April 2021 deadline or at most with a one-day delay. 24 countries submitted their plans by the end of June 2021, while Malta submitted its plan in July 2021, Bulgaria in October 2021, and the Netherlands in July 2022.

Comparing the national plans is challenging, because they present data in very different structures. The number and definition of headline categories and the availability of summary information about sub-categories varies from country to country. Nevertheless, the biggest challenge of cross-country comparison is the definition of non-overlapping spending categories, because a particular investment could support various purposes as defined by the Article 3 of the RRF Regulation, for example green, social and inclusive growth as well as policies for the next generation. In our reading, cross-country comparisons of recovery plans published so far by other researchers do not pay enough attention to the multiplicity of purposes.

The current version of this dataset focuses on planned investment by countries that have submitted their plans. We will update this dataset as remaining countries submit their plans and augment this analysis with a comparison of proposed reforms, also in light of the country-specific recommendations (CSRs) made in the context of the European Semester.

Overall green and digital shares in investment

While not all countries specify the exact green and digital components of sub-categories, every country presents the overall shares of green and digital components which have to reach at least 37% and 20% respectively. We therefore start with the overall comparison of green, digital and other goals in total planned spending (Figure 1) and present more detailed alternative categorisations below.

It is important to bear in mind three caveats concerning the current version of the dataset:

  • We do not examine whether spending plans constitute new spending, or cover spending already planned before the pandemic which will now use the RRF money instead of national tax revenue. An analysis of additionality is crucial in assessing the possible economic impact of the RRF.
  • We do not examine whether spending declared by the plans as ‘green’ and ‘digital’ complies with the relevant taxonomy defined by the RRF Regulation.
  • While ‘green’, ‘climate’, and ‘environmental’ spending have specific definitions, the particular categorisation of national spending lines is not always clear. Hence, we simply use ‘green’ for all kinds of climate and environment-related spending items and do not separate the particular components related to climate change.

With these caveats in mind, Figure 1 compares the green and digital components of the plans. Significant diversity emerges. Countries that receive relatively smaller amounts from the RRF as a share of their GDP presented plans that concentrate on green and digital spending (Germany, Luxembourg and Denmark), while countries that receive larger amounts presented more diverse plans with higher ‘other’ (non-green and non-digital) shares of spending.

Figure 1: Overall resource allocation in national recovery and resilience plans (% of total and € billions)


Source: Bruegel based on submitted national recovery plans. Note: the numbers in brackets after the country names indicate the total € amount of the plan to be financed by the Recovery and Resilience Facility, while the numbers over the bars show the € value of the components (in billions). For Italy, both RRF grants and loans are included. Note there is some overlap between green and digital spending, but the level of detail in most national plans does not allow us to properly account for these overlaps. This figure reports the headline green and digital numbers and does not consider their overlaps.

Submission dates, grants and loans requested

With the exceptions of Latvia, all countries requested the estimated full amount of grants or more which were included as indicative amounts in Annex IV of the RRF Regulation (column ‘Estimated RRF grants’ in Table 1). These estimates were based on the Commission’s autumn 2020 forecasts, while the final amounts were calculated on 30 June 2022 using actual data. Any excess request over the maximum amount available for a particular country will be financed by that country.

So far seven countries have requested loans. Of these seven countries Greece, Italy and Romania have requested the full amount of loans available to them, while Cyprus, Poland, Portugal and Slovenia have requested between 16% and 37% of the loans available to them. According to Article 14 of the RRF Regulation, countries can request loan support until 31 August 2023, so more countries may do so.

Panel A of Figure 2 indicates that the cross-country allocation of grants strongly depends on the level of development, suggesting that the RRF could contribute to convergence on the part of poorer countries. Panel B shows that there is practically no association with expected GNI growth from 2019 to 2021, highlighting that the RRF is not really a crisis-alleviation tool.

National recovery plans figure 2


Source: Bruegel based on RRF Regulation (grant allocation in euros), and the spring 2021 Commission forecast (GNI in euros, GNI per capita at purchasing power standards [PPS], and real growth of GNI). Note: the trendline (indicated with dark-red colour) was calculated by fitting a third-order polynomial.

Categorisation of investments according to the RRF Regulation

We compare the spending plans in more detail than the green / digital / other categorisation presented in Figure 1. We use three straightforward classifications for comparing the national plans. Since none of these classifications are ideal (as we explain below), we complemented them with our own classification.

The first classification we use considers the six pillars defined in Article 3 of the RRF Regulation:

  • Green transition;
  • Digital transformation;
  • Smart, sustainable and inclusive growth including economic cohesion, jobs, productivity, competitiveness, research, development and innovation and a well-functioning internal market with strong SMEs;
  • Social and territorial cohesion;
  • Health and economic, social and institutional resilience, with the aim of, inter alia, increasing crisis preparedness and crisis response capacity; and
  • Policies for the next generation, children and youth such as education and skills.

The main problem with this classification is that several pillars overlap. For example, ‘industry decarbonisation’ fosters green transition (1st pillar) and sustainable growth (3rd pillar). ‘Providing digital equipment for teachers’ supports digital transformation (2nd pillar) and education policy (6th pillar). It is also difficult to categorise a programme that aims to subsidise the hiring of disabled people, for example, because that could be labelled as inclusive growth (3rd pillar) or social cohesion (4th pillar), or even as social resilience (5th pillar). Moreover, some of the projects foster both green transition and digital transformation, such as ‘support to aeronautical industry’, though the full amount of a particular heading or subheading is not green or digital. For most investment items, the green and digital components are only partial.

Thus, when using this categorisation, we assigned either one or two pillars to each spending category based on our judgement. When we assigned two pillars (eg 1. green and 3. sustainable growth), a multiplicity of categories emerged, which we report in Table 2 and Figure 3.

Categorisation of investments according to the flagship areas defined by the Commission

An alternative classification is the seven flagship areas for investment and reforms defined by the Commission:

  • Power up (clean technologies and renewables);
  • Renovate (energy efficiency of buildings);
  • Recharge and refuel (sustainable transport and charging stations);
  • Connect (roll-out of rapid broadband services);
  • Modernise (digitalisation of public administration);
  • Scale-Up (data cloud capacities and sustainable processors);
  • Reskill and upskill (education and training to support digital skills).

The advantage of this classification is that the overlaps between the seven items are small (although the partial overlap between green and digital projects remains) and each spending item can be placed into a single category with a good level of certainty. The disadvantage is that this classification does not include all green and digital goals and lacks all non-green and non-digital goals like social inclusion or research (if these do not have a green or digital component). We therefore add three additional categories:

  • Other green;
  • Other digital;
  • Uncategorised.

Categorisation of investments according to economic sector

The third categorisation we use is a statistical classification of economic activities from the national accounts dataset, NACE (Nomenclature statistique des activités économiques dans la Communauté européenne). The advantage here is that there is no overlap between the categories, while the disadvantage is that it is not always clear how to categorise a particular spending item. For example, ‘support to small and medium-sized enterprises’ is not sector-specific. We therefore added an ‘uncategorised’ category. Also, some spending items could be related to multiple industries: high-speed trains, for example, includes the building of railway stations (sector F: construction) and the deployment of trains (sector H: transportation and storage). Sufficiently detailed data to disentangle the various industries within a particular spending item is often not available.

Categorisation of investments according to our new classification

Because of the shortcomings of the above-discussed three alternative ways of categorisation of spending, we have developed our own categorisation. Our categorisation aims to offer more granularity in classifying spending while still being able to have aggregate comparisons in more aggregated categories. We thus classify spending in three main categories, which are divided into several subcategories.

Green transition:
  • Biodiversity (includes land restoration, marine and maritime)
  • Air and water quality (includes sewage)
  • Circular economy (includes waste management)
  • Climate change adaptation projects
  • Green tech innovation
  • Renewable energy sources (includes wind and solar, and alternative fuels)
  • Buildings’ energy efficiency
  • Non-building energy efficiency (eg machines, production processes)
  • Hydrogen
  • High speed trains
  • Public transport
  • Other sustainable transport infrastructure (excludes highways and roads)
  • Electric mobility (charging stations and vehicles incentives)
  • Electricity grids
  • Sustainable agriculture
  • Green tax incentives
Digital transformation:
  • Connectivity
  • Digital-related investment in research and development
  • Digital skills and digital inclusion
  • Digital public sector
  • Digitalisation of businesses
  • Investment in digital capacities and deployment of advanced technologies (includes cybersecurity)
  • Greening the digital sector (energy efficiency of data centres and networks)
Social, economic, and institutional development:
  • Economic, social and territorial cohesion
  • Labour market and job creation
  • Strengthening of the financial system
  • Infrastructure investment
  • R&D and innovation (non-green and non-digital)
  • SMEs
  • Health
  • Crisis preparedness and resilience
  • Justice and combating corruption
  • Education and skills (non-digital)
  • Culture and tourism
  • Sports
  • Uncategorised

Even in our classification, there is overlap between some of spending items, which is especially significant for the plans with insufficiently detailed information. To account for overlaps, we allowed each spending item to have a main and a secondary sub-category. Accounting for all the possible combinations, we ended up with the hybrid main categories that are shown in the table and chart below.

Time profile of planned spending

Surprisingly, only a few countries presented an annual breakdown of planned spending in their recovery plans, while Spain provided the annual breakdown in its 2021 Stability Programme (Table 4.3.2 on page 79).

The European Commission's assessments of the recovery and resilience plans

The Commission should assess the proposed recovery and resilience plans within two months of their official submission and make a proposal for a Council implementing decision (Article 19 and Annex 5 on ‘Assessment guidelines for the Facility’ of the RRF Regulation). The assessment is based on eleven criteria (see their list in the header of Table 6, while the detailed description is presented in Annex V of the RRF Regulation).

Nine of the eleven categories are rated on a scale of 3, with rating A when a criterion is met to a large extent, B when it is met to a moderate or medium extent, and C when it is met to a small extent. Exceptions are the criterion on ‘do not do significant harm to the environment’ (criterion 4) and ‘preventing corruption, fraud and conflicts of interest’ (criterion 10), which are rated either A or C. The Commission’s assessments are available here, while Table 6 displays the ratings.

Among the 27 plans, 24 obtained exactly the same score (10 A ratings and 1 B rating), while Belgium and Estonia got 9 A ratings and 2 B ratings and Czechia 8 A and 3 B ratings. Thus, the Commission assessed these plans very positively.

For ‘cost justification’ (category 9), all 27 countries obtained a B rating. This means that according to the Commission, “The justification provided by the Member State on the amount of the estimated total costs of the recovery and resilience plan is reasonable and plausible and is in line with the principle of cost efficiency and is commensurate to the expected national economic and social impact” is met only “to a medium extent” by all EU countries.

Some or more of the arguments below were listed to justify the medium rating:

  • cost breakdowns show varying degrees of detail and depth of calculation;
  • there are some gaps in the information and evidence provided on reasonability and plausibility of the estimated costs;
  • sometimes the methodology used is not sufficiently well explained and the link between the justification and the cost itself is not fully clear;
  • in some cases, the methodologies and assumptions are less robust;
  • some projects are not sufficiently substantiated with cost of comparable projects, or the evidence cited could not be accessed;
  • funding criteria and beneficiaries are sufficiently detailed;
  • there is significant potential overlap between the Recovery and Resilience Plan and other EU facilities, such as the European Structural Investment Funds (ESIF) and Connecting Europe Facility (CEF), but details are not always clear enough or simply not provided on whether double EU funding will be avoided.

While reading through the detailed assessments of cost justifications, we noticed that for some countries only one of above listed factors are mentioned and it is hard to find any critical wording in the assessment, while for others several of them were mentioned and several sentences expressed criticism. This makes us wonder why all countries obtained the same B rating.

Additionally, Belgium obtained a B rating for coherence, because:

  • the plan could have better exploited the whole potential of some investments through more far-reaching complementary reforms;
  • the plan could have provided greater incentives to trigger private renovations in particular with a strong commitment to reform energy taxes on heating fuels with a view to sending the right price signals;
  • there is room to improve the coherence of measures in the sustainability and productivity axes;
  • many reforms and investments are not applied uniformly and consistently across the country, which would be warranted.

This page provides a number of Bruegel datasets for public use. Users can freely use our data in its unchanged form or after any transformation for any purpose and can freely distribute it, provided that proper attribution is made to the source, but not in any way that suggests that Bruegel endorses the user or their use of the data.

This dataset will be regularly updated and augmented. Comments to improve the coverage and the estimates are welcome. Please send your comments to [email protected]

About the authors

  • Zsolt Darvas

    Zsolt Darvas is a Senior Fellow at Bruegel and part-time Senior Research Fellow at the Corvinus University of Budapest. He joined Bruegel in 2008 as a Visiting Fellow, and became a Research Fellow in 2009 and a Senior Fellow in 2013.

    From 2005 to 2008, he was the Research Advisor of the Argenta Financial Research Group in Budapest. Before that, he worked at the research unit of the Central Bank of Hungary (1994-2005) where he served as Deputy Head.

    Zsolt holds a Ph.D. in Economics from Corvinus University of Budapest where he teaches courses in Econometrics but also at other institutions since 1994. His research interests include macroeconomics, international economics, central banking and time series analysis.

  • Marta Domínguez-Jiménez

    Marta Domínguez Jiménez was a Research Analyst at Bruegel. Her research focuses primarily on monetary policy, financial systems and international trade and capital flows. She has published on these issues for Bruegel, in academic journals and European Parliament and Commission reports, among others.

    She holds a bachelor from the University of Oxford, where she specialised in international macroeconomics and monetary economics, and a Master's from the College of Europe in Bruges. Before joining Bruegel, she was an Analyst within the Markets division of Citigroup in London, where she worked on the structuring of bespoke fixed income products and developing systematic quantitative investment strategies.

    Marta is fluent in Spanish and English, and proficient in German and French

  • Ashling Devins

    Ashling works at Bruegel as a Research Intern. She studied Economics at Emory University, and is working on her second bachelor's in Materials Science and Engineering at Georgia Institute of Technology.

    Besides Bruegel, Ashling has been working on an ongoing safety-related project with some classmates and a Georgia Tech Professor, focused on safety analytics, personal protective equipment and safety management software.

    Ashling is an Irish and American citizen. She has a working proficiency in French.

  • Monika De Ridder (Grzegorczyk)

    Monika worked at Bruegel as a Research Analyst until August 2022. Monika is completing her second master’s degree in Models and Methods of Quantitative Economics at Paris 1 Pantheon Sorbonne and UCLouvain. She holds a BSc in finance and a MA in Political Science. Her research interests include monetary policy, financial regulations, and structural reforms.

    Prior to Bruegel, Monika worked as a Junior Economist at OECD on the qualitative and quantitative assessment of the implementation of structural policies and recommended actions. She was able to apply new machine learning methods such as Natural Language Processing for textual analysis.

    Monika was a trainee at governmental bodies (the Polish Finance Ministry, Ministry of the Interior and Administration, and the Polish delegation to OECD) and worked for non-governmental organisation (Foundation Institute for Strategic Studies). She also gained her experience through research assistance at the Paris School of Economics on Macroeconomic imbalances procedure (published as European Parliament Study).

  • Lionel Guetta-Jeanrenaud

    Lionel worked at Bruegel as a Research Assistant until August 2022. He studied economics at the Ecole normale supérieure de Lyon, in France. Before joining Bruegel, Lionel worked as a research assistant at the Department of Economics of Harvard University.

    His Master’s thesis investigated the impact of newspaper closures on anti-government sentiment in the United States. In addition to media economics and political economy, his research interests include fiscal policy and the digital economy.

    Lionel is a dual French and American citizen.

  • Surya Hendry

    Surya worked at Bruegel as a Research and Communications intern. She is currently an undergrad at Stanford, where she studies Political Science, specializing in Political Economy and Development. In the past, she has conducted research on U.S. elections, multilateral development banks, and Iranian history.

    Outside of her education, Surya has created podcasts for national radio programs and local radio stations. She currently helps to produce Bruegel's podcast, The Sound of Economics.

  • Mia Hoffmann

    Mia worked at Bruegel as a Research Analyst until August 2022. She studied International Economics (BSc) at University of Tuebingen, including one semester at the Università di Torino, and holds a Master’s degree in Economics from Lund University.

    Before joining Bruegel Mia worked in the international development sector. As a Bluebook Trainee she worked at the European Commission’s Directorate-General for International Cooperation and Development and previously interned at the German development bank DEG, working on credit analysis and restructuring.

    Her previous research focused on the impact of migration on economic growth and analyzed the effects of childcare policy on household bargaining. Her current research interests involve issues related to trade, labor markets and inequality.

    Mia is a German native speaker, is fluent in English and has good working knowledge in French and Italian.

  • Klaas Lenaerts

    Klaas worked at Bruegel as a Research Analyst until August 2022. He holds a Master in Economics from the KU Leuven and in European Economic Studies from the College of Europe. Additionally, he spent one semester at Uppsala University.

    Klaas has a broad background in economics and European affairs. Before joining Bruegel he did a traineeship at the Permanent Representation of Belgium to the EU, where he worked on enlargement discussions, and at the European Securities and Markets Authority in Paris, where he contributed mainly to the work of the Risk Analysis and Economics department on such topics as crypto regulation and sustainable finance.

    His fields of interest include European climate policy and Eurozone governance, as well as external relations and trade. He is fluent in Dutch and English and advanced in French and German.

  • Tom Schraepen

    Tom worked at Bruegel as a Future of Work and Inclusive Growth Consultant until May 2023. He obtained his BSc in Business Engineering and his MSc in Applied Economics from KU Leuven. He wrote his master’s thesis in the field of innovation economics.

    At Bruegel, he mainly works on the Future of Work and Inclusive Growth project. He researches the twin transition, the digital economy and labour market inequality. 

    Tom is a Belgian citizen. He is fluent in Dutch and English, and advanced in French.

  • Alkiviadis Tzaras

    Alkiviadis was a Research Assistant at Bruegel. He is an experienced Data Analyst focusing in causal inference, with an academic background in Economics and professional experience in the International Grants management sector. He has strong expertise in Results‑Based Management and programming.

    Prior to joining Bruegel he worked for the Financial Mechanism Office of the EFTA secretariat in Brussels in the Results & Evaluation unit. He was responsible for creating frameworks that measured the progress and intervention logic of various programmes in various sectors such as Education, Innovation, Research and others. He was also responsible for designing centralised databases and reports. Apart from that, he has also worked as a teacher assistant in Full-Stack Web Development and a translator of academic economic textbooks from English to Greek.

    Alkiviadis holds a Master’s degree in Economic Analysis from the University of Ioannina. He is fluent in Greek and English and he has conversational knowledge of French.

  • Victor Vorsatz

    Victor works at Bruegel as a Research Intern. After finishing his BA in International Affairs at the University of St. Gallen, he worked for a sustainability consultancy in Munich. Currently, he is pursuing a master’s degree at the Johns Hopkins University, where he focuses on energy and climate policy as well as economic policy in general. Furthermore, Victor’s past research included the analysis of the correlation of digital surveillance and trust, distributional implications of tax policies, and the mapping of the convergence of EU citizen’s values.

    Before Bruegel, Victor organised two instances of a conference on Social Entrepreneurship at the University of St. Gallen, while also facilitating a practically oriented class on sustainable business models there.

    Victor is a German native speaker, fluent in English, and has a working knowledge of Italian.

  • Pauline Weil

    Pauline worked at Bruegel as a Research Analyst until September 2022. She holds a bachelor in Political Science and a master’s degree in International Trade and Finance from Sciences Po Lille. She also studied an MSc in Political Economy of Europe at the London School of Economics.

    Her research interests include monetary policy, sovereign debt sustainability, trade and the energy transition. Pauline’s two regions of expertise are Europe and Asia.

    She wrote a master’s thesis on the European Stability and Growth Pact by focusing on Greece’s adoption of the euro and its government debt crisis. And her second master’s thesis questioned the political and economic sustainability of the Franc CFA currency in the West African Economic and Monetary Union (WAEMU) in the context of European integration.

    Prior to Bruegel, Pauline was a Junior Economist for the credit insurer Coface where she provided country risk analysis on Europe, working from Paris, and then on Asia, from Hong Kong. She also pursued the Blue Book Traineeship at the European Commission, working for DG DEVCO in the Directorate for Asia.

    Pauline is fluent in French and English and has a good command of Spanish.

  • Lennard Welslau

    Lennard is a Research analyst at Bruegel. His research interests lie in the fields of macroeconomics, international economics, and data science. He studied Philosophy, Politics and Economics in Freiburg and Buenos Aires and holds an MSc in Economics from the University of Copenhagen. In his thesis, he used a small open economy heterogeneous agent New Keynesian (HANK) model to investigate the role of wage flexibility in countries facing external demand and interest rate shocks.

    Prior to joining Bruegel, he worked as a trainee with the European Central Bank, held research assistant positions at the Walter Eucken Institute and the Copenhagen Business School, and contributed to the work of the UN Economic Commission for Latin America and the Caribbean (ECLAC) as a research consultant, working on empirical trade modelling.

    Lennard is a native German speaker and is fluent in English and Spanish.

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