Study to develop EU level Simplified Cost Options (SCOs) and other EU level results-based tools in the programming period 2021-2027 : final study report.
Date: 2023-08-23
Institutions: Directorate-General for Regional and Urban Policy
Subjects: EU Member State, administrative expenditure, energy efficiency, energy research, energy saving, improvement of housing, innovation, research and development, research report, simplification of formalities, small and medium-sized enterprises

Study to develop EU level Simplified Cost 
Options (SCOs) and other EU level results-based 
tools in the programming period 2021-2027 

Final Study Report 

Written by PPMi in collaboration with t33 
July – 2023 

  
                                                                                                                                   
     
EUROPEAN COMMISSION 

Directorate-General for Regional and Urban Policy 
Directorate F - Better implementation, Closure and Programme Implementation III 
Unit REGIO.F.1 — Better implementation and Closure 

Contact: Charlie Grant 

E-mail:  REGIO-F1-HOU@ec.europa.eu  

European Commission 
B-1049 Brussels 

 
 
EUROPEAN COMMISSION 

Study to develop EU level Simplified Cost 

Options (SCOs) and other EU level results-based 
tools in the programming period 2021-2027 

Final Study Report 

2023 

EN 

Directorate-General for Regional and Urban Policy 

 
Manuscript completed in July 2023 

1st edition 

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ISBN 978-92-68-07106-9   

doi: 10.2776/46668 

KN-09-23-408-EN-N 

 
 
 
 
 
 
 
 
Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

Table of contents 

1.2. 
2. 
2.1. 

List of abbreviations ............................................................................................................................................ 6 
Glossary of terms ................................................................................................................................................. 8 
Executive summary ............................................................................................................................................ 10 
Introduction ........................................................................................................................................................ 12 
Methodology ....................................................................................................................................... 15 
1. 
EU-level SCOs ...................................................................................................................................... 15 
1.1. 
1.1.1.  Design ................................................................................................................................... 16 
1.1.2. 
Preparatory activities .......................................................................................................... 18 
1.1.3.  Data collection, cleaning and preparation for the analysis............................................ 22 
Feasibility analysis of potential SCO alternatives ........................................................... 24 
1.1.4. 
Calculation and adjustment methods ............................................................................... 24 
1.1.5. 
1.1.6. 
Consultations to test and validate study outcomes ........................................................ 27 
EU-level result-based tools ................................................................................................................ 32 
Study findings .................................................................................................................................... 39 
Area 1: SCOs for Energy Efficiency & Renewable Energy ............................................................ 39 
Sub-area 1A: Energy Efficiency & Renewable Energy in SMEs .................................... 41 
2.1.1. 
2.1.2. 
Sub-area 1B: Energy Efficiency & Renewable Energy in Buildings  
(public and housing sectors) .............................................................................................................. 46 
Area 2: SCOs for Research & Innovation activities ........................................................................ 51 
Sub-area 2A: Knowledge / Technology Transfer Activities of SMEs ........................... 54 
2.2.1. 
2.2.2. 
Sub-area 2B: Innovation Vouchers for SMEs for R&D Implementation ...................... 60 
Sub-area 2C: Research Development & Innovation Research Projects ........................ 68 
2.2.3. 
Area 3: SCOs for SME Growth & Competitiveness ........................................................................ 71 
Sub-area 3A: Networking Activities of SMEs ................................................................. 72 
2.3.1. 
2.3.2. 
Sub-area 3B: Consultancy / advisory services to elaborate an  
internationalisation strategy .............................................................................................................. 77 
Feasibility checks ................................................................................................................................. 81 
FC1: Promoting sustainable multimodal urban mobility .............................................. 81 
2.4.1. 
FC2: Skills for smart specialisation and transition .......................................................... 98 
2.4.2. 
2.4.3. 
FC3: Climate change adaptation and risk prevention .................................................. 110 
3. 
Conclusions ....................................................................................................................................... 129 
Annex 1. Historical data collected from the Member States .................................................................... 132 
Annex 2. Operational Programmes sampled for historical data collected ............................................ 133 
Annex 3. Data quality assessment of Areas 1, 2 and 3 ............................................................................... 143 

2.2. 

2.3. 

2.4. 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

List of figures 

Figure 1. SCO development process ............................................................................................................................. 16 
Figure 2. Overlap of intervention fields in the sub-area of sustainable urban mobility ........................................ 85 
Figure 3. Sustainable urban mobility intervention logic ............................................................................................ 86 
Figure 4. Skills development for smart specialisation, industrial transition, entrepreneurship,  
and adaptability of enterprises to change intervention logic .................................................................................. 101 
Figure 5. Overlap of intervention fields in the sub-area of climate change adaptation and risk prevention 
(Venn diagram) .............................................................................................................................................................. 114 
Figure 6. Climate change adaptation and risk prevention intervention logic ....................................................... 115 

List of tables 

Table 1. Typologies of activities included in data collection form ............................................................................ 21 
Table 2. Proposed explanatory indicators for areas of the study .............................................................................. 25 
Table 3. Proposed inflation indicators for areas of the study .................................................................................... 26 
Table 4. The ranking order of pre-selected investment areas .................................................................................... 33 
Table 5. Alternatives considered for further SCO development under Area 1 ....................................................... 39 
Table 6. Proposed SCO rates in sub-area 1A ................................................................................................................ 43 
Table 7. Assessment of historical data availability in sub-area 1B ............................................................................ 46 
Table 8. Proposed SCO rates in sub-area 1B ................................................................................................................ 49 
Table 9. Alternatives considered for further SCO development under Area 2 ....................................................... 51 
Table 10. Assessment of historical data availability in sub-area 2A ......................................................................... 55 
Table 11. Proposed SCO rates in sub-area 2A .............................................................................................................. 57 
Table 12. Calculation process for the development of SCOs for sub-area 2B .......................................................... 63 
Table 13. Member States which provided information on the location of the international event for sub-area 
3A ....................................................................................................................................................................................... 72 
Table 14. Networking activities of SMEs (Sub-Area 3A): identification and exclusion of the outliers ................ 74 
Table 15. Proposed SCO rates for sub-area 3A ............................................................................................................ 74 
Table 16. Consultancy / advisory services to elaborate an internationalisation strategy (sub-area 3A): 
identification and exclusion of the outliers .................................................................................................................. 78 
Table 17. Examples of supported operations / projects in the area of sustainable urban mobility ...................... 83 
Table 18. Common ERDF / CF indicators in the area of sustainable urban mobility ............................................. 87 
Table 19. Overview of selected indicators in the area of sustainable urban mobility ............................................ 88 
Table 20. Criteria for feasibility assessment ................................................................................................................. 89 
Table 21. Assessment of selected indicators in the area of sustainable urban mobility ......................................... 89 
Table 22. Overview of potential data sources to establish financial amounts in the area of sustainable urban 
mobility ............................................................................................................................................................................. 94 
Table 23. Summary of potential FNLC solutions in the area of sustainable urban mobility................................. 96 
Table 24. Examples of supported operations / projects in the area of skills for smart specialisation and 
transition ........................................................................................................................................................................... 98 
Table 25. Criteria for feasibility assessment ............................................................................................................... 102 
Table 26. Common ERDF / CF indicators in the area of skills for smart specialisation and transition.............. 103 
Table 27. Overview of selected indicator in the area of skills for smart specialisation and transition .............. 104 
Table 28. Overview of potential data sources to establish financial amounts in the area of skills for smart 
specialisation and transition ......................................................................................................................................... 107 
Table 29. Summary of potential FNLC solutions in the area of skills for smart specialisation and transition . 109 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

Table 30. Intervention fields in the area of climate change adaptation and risk prevention .............................. 112 
Table 31. Common ERDF/CF indicators in the area of climate change adaptation and risk prevention .......... 116 
Table 32. Overview of most common indicators in the area of climate change adaptation and risk prevention
 .......................................................................................................................................................................................... 118 
Table 33. Criteria for feasibility assessment ............................................................................................................... 119 
Table 34. Assessment of selected indicators in the area of climate change adaptation and risk prevention .... 119 
Table 35. Overview of potential data sources to establish financial amounts in the area of climate change 
adaptation and risk prevention .................................................................................................................................... 124 
Table 36. Summary of potential FNLC solutions in the area of climate change adaptation and risk prevention
 .......................................................................................................................................................................................... 127 
Table 37. Data collection process for Area 1 ............................................................................................................... 133 
Table 38. Data collection process for Area 2 ............................................................................................................... 136 
Table 39. Data collection progress for Area 3 ............................................................................................................. 138 
Table 40. Overview of data coverage per sub-area in Area 1 .................................................................................. 144 
Table 41. Typologies of activities supported in sub-area 1A ................................................................................... 147 
Table 42. Typologies of activities supported in sub-area 1B.1 ................................................................................. 148 
Table 43. Typologies of activities supported in sub-area 1B.2 ................................................................................. 149 
Table 44 Overview of cost data quality in selected sub-areas ................................................................................. 150 
Table 45. Availability of the breakdown of costs at activity level in sub-area 1A ................................................ 152 
Table 46. Availability of the breakdown of costs at activity level in sub-area 1B.1 .............................................. 154 
Table 47. Availability of the breakdown of costs at activity level in sub-area 1B.2 .............................................. 155 
Table 48. Indicators used by member states in sub-area 1A* ................................................................................... 156 
Table 49. Indicators used by member states in sub-area 1B.1 .................................................................................. 157 
Table 50. Indicators used by member states in sub-area 1B.2 .................................................................................. 158 
Table 51. Specific indicators used by Member States across all sub-areas ............................................................. 159 
Table 52. Overview of data from alternative sources ............................................................................................... 162 
Table 53. Availability of key data points needed for SCO development ............................................................... 163 
Table 54. Criteria used for assessing the collected historical data .......................................................................... 165 
Table 55. Overview of data coverage per sub-area ................................................................................................... 166 
Table 56. Main typologies of activities supported in sub-area 2A* ......................................................................... 167 
Table 57. Main typologies of activities supported in sub-area 2B* ......................................................................... 169 
Table 58. Main typologies of activities supported in sub-area 2C* ......................................................................... 170 
Table 59. Macro-categories in sub-area 2A ................................................................................................................. 172 
Table 60. Macro-categories in sub-area 2B ................................................................................................................. 173 
Table 61. Macro-categories in sub-area 2C ................................................................................................................. 174 
Table 62. Availability of breakdown of costs at the activity level ........................................................................... 176 
Table 63. Historical data provided on most common outputs / results indicators per sub-area ........................ 178 
Table 64. Relevant variables from Community Innovation Survey ........................................................................ 179 
Table 65. Criteria used for assessing the collected historical data .......................................................................... 180 
Table 66. Networking activities of SMEs (sub-area 3A): overview of data coverage ........................................... 181 
Table 67. Consultancy / advisory services to elaborate an internationalisation strategy (sub-area 3B): overview 
of data coverage ............................................................................................................................................................. 183 
Table 68. Networking activities of SMEs (sub-area 3A): type of eligible activities ............................................... 184 
Table 69. Consultancy / advisory services to elaborate an internationalisation strategy (sub-area 3B): type of 
eligible activities ............................................................................................................................................................. 185 
Table 70. Networking activities of SMEs (sub-area 3A): type of indicators ........................................................... 186 
Table 71. Consultancy / advisory services to elaborate an internationalisation strategy (sub-area 3A): type of 
indicators ......................................................................................................................................................................... 187 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

List of boxes 

Box 1. Sampling frame conditions ................................................................................................................................. 18 
Box 2. Approach to interquartile analysis for the purpose of removing outliers ................................................... 23 
Box 3. Hypothetical scenario for outlier analysis based on expert judgement ........................................................ 24 
Box 4. Participant vs Employed ................................................................................................................................... 106 
Box 5. Programme Galicia ERDF 2021-2027 (Spain) ................................................................................................. 113 
Box 6. Croatia's National Recovery and Resilience Plan .......................................................................................... 123 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

List of abbreviations 

CF 

CO2 

DCF 

Cohesion Fund 

Carbon Dioxide 

Data Collection Form  

DG ENER 

Directorate-General for Energy 

DG REGIO 

Directorate-General for Regional and Urban Policy 

DG RTD 

Directorate-General for Research and Innovation 

EC 

EE 

European Commission 

Energy Efficiency 

EPC 

Energy Performance Certificates  

ERDF 

European Regional Development Fund 

EU 

European Union 

FNLC 

Financing Not Linked to Costs 

FTE 

GHG 

HICP 

IB 

ICT 

LCI 

MA 

MS 

Full-Time Equivalent  

Greenhouse Gas 

Harmonised Index of Consumer Prices 

Intermediary Body 

Information and Communications Technology 

Labour Cost Index 

Managing Authority 

Member State 

MSCA 

Marie Skłodowska-Curie Actions  

OECD  

Organization for Economic Co-operation and Development 

OLS 

Ordinary Least Squares 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

OP 

PKM 

PLI 

PO 

Q&A 

R&D 

R&I 

RDI  

RES 

SCO 

SME 

TN 

ToR 

Operational Programme 

Passengers-Kilometres 

Price Level Index 

Policy Objectives  

Question & Answer 

Research & Development 

Research & Innovation 

Research Development and Innovation 

Renewable Energy Sources  

Simplified Cost Option 

Small and Medium-sized Enterprise 

Transnational Network  

Terms of Reference 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

Glossary of terms 

Accuracy refers to how close a set of measurements are to their true value. 

Activity is an action that can be costed under a single SCO. 

Area is a policy area of interest identified by DG REGIO.  

Beneficiary is the organisation, which may be private, public or in the third sector, responsible for initiating 
and/or implementing an operation / financial instrument.  

Call (for proposal) is an open invitation for funding issued by the European Union. It is a financial contribution 

aiming to strengthen and leverage actions or projects that support the EU policies in the most effective way. 

Completeness refers to the condition of possessing all data points needed for calculations. 

Consistency is when values across different locations are identical or have a high degree of similarity. 

Costs (Direct) are costs which can be attached exclusively to a single service or product (in the context of SCO, 

this service is usually an activity). 

Costs (Indirect) cannot be tied to a specific activity but are necessary to deliver that activity. For example, the 

marginal increment in administrative costs resulting from an additional activity taking place may be difficult 

to trace. 

Creaming of participants, also known as cherry-picking, implies selecting participants which are most likely to 
successfully enter and exit the activity and thus generate the intended outputs or results. 

Extrapolation is a method of statistical inference which infers from values observed outside the sample. 

Financing  Not  Linked  to  Costs  is  the  payment  method  for  grants  and  repayable  assistance  in  which  the 

reimbursement of expenditure is based on the fulfilment of pre-established conditions or results to be achieved. 

Flat  rate  financing  are  specific  categories  of  eligible  costs  which  are  clearly  identified  in  advance  and  are 

calculated by applying a percentage, fixed ex ante to one or several other categories of eligible costs. 

Historical data are administrative records kept by  Managing Authorities and Intermediary bodies  on calls / 

projects 

Interpolation is a method of statistical inference which infers from values observed within the sample. 

Lump sums are fixed, single payments based on achievement of agreed incurrence of costs. 

Observations are, in statistics, one occurrence of something being measured. 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

Operation  is  a  project,  contract,  action  or  group  of  projects  selected  by  the  Managing  Authority  of  the 
programmes concerned, or under its responsibility, that contributes to the objectives of one or more priorities 
of a programme. 

Outlier is a specific data point that deviates substantially from the remaining observations. 

Parking is the practice where providers attempt  to minimise costs by providing minimal assistance to those 

with the lowest anticipated outcomes, while concentrating their resources on those who have better prospects. 

Participant(s) are the persons who benefit from the activity. Participants may include  persons who started a 
training but did not complete it. Participants should mainly draw from the target group. 

Path dependence is the process by which previous events or decisions place limitations or influence subsequent 

events or decisions. 

Managing Authority is an institution in a Member State responsible for the strategic direction and financial 
management of a programme in the context of ERDF/CF.  

Simplified Cost Options  (SCO) are a way of reimbursing grants and repayable  assistance where  instead of 

reimbursing  ‘real  costs’,  SCO  allow  reimbursing  expenditure  according  to  predefined  methods  based  on 

process, outputs or results. SCO can take the form of flat rate financing, unit costs, and lump sums. 

Slicing of operations (reduced duration, cheaper delivery methods, etc.) is a perverse incentive risking to result 
in reduced quality of the activity / operation. 

Stakeholder is an individual or group with direct interest in the final outcomes of this study.  

Target group is the group of persons who are eligible to participate in the activity.  

Triangulation is the practice of employing multiple data sources in order to address a research question. 

Unit costs are the cost per unit of input, output or result generated.  

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

Executive summary 

This study was launched with a purpose of developing EU-level Simplified Cost Options (SCOs) and other EU-
level results-based tools in multiple areas supported by the European Regional Development Fund (ERDF) and 
Cohesion Fund (CF) in the programming period 2021-2027. The methodology employed in this study is aligned 
with  the  provisions  of  Article  94  of  the  Common  Provisions  Regulation1.  More  specifically,  it  builds  on  the 
analysis of verified historical data collected at national and/or regional levels and statistical inference where 
historical data are insufficient or unavailable. The study elaborates on potential EU-level SCOs in the following 
areas relevant to ERDF/CF:  

•  Energy Efficiency and Renewable Energy (Area 1), focusing on small or medium enterprises (SMEs) 
(sub-area 1A), housing sector (sub-area 1B.1) and non-residential (public) sector (sub-area 1B.2); 
•  Research and Innovation activities (Area 2), in particular knowledge / technology transfer activities 
of SMEs (sub-area 2A), innovation vouchers for SMEs for R&D implementation (sub-area 2B) and 
Research Development & Innovation research projects (sub-area 2C). 

•  SME Growth & Competitiveness (Area 3), in particular networking activities of SMEs (sub-area 3A) 
and consultancy / advisory services to SMEs for digital & green transformation (sub-area 3B). 

Notably, sub-area 1B was initially divided into two separate sub-areas – housing and non-residential public 
buildings sectors. However, as operations in both sectors share an identical set of activities, it has been decided 
to merge the two to attain a more robust sample of historical data. The underlying process of historical data 
collection and analysis consisted of the following steps: 

1.  Undertaking preparatory activities, such as exploratory research on the relevant measures; 

developing structured data collection forms; organising stakeholder consultations to test data 
collection forms; selecting a representative sample of Operational Programmes (OP) to source data 
from. 

2.  Collecting historical data on relevant interventions at national and regional levels. 
3.  Collecting (proxy) data from alternative sources that could be used to triangulate, complement or 

replace the historical data where it is lacking. 

4.  Interpreting, cleaning and analysing the collected historical data to identify any gaps, discrepancies 

and/or outlier cases and re-engaging the national experts in resolving the data shortcomings. 
5.  Applying and elaborating SCO methodologies for assessing the feasibility of EU-level SCO 

development, including methods and techniques for determining the SCO rates / amounts for the 
Member States (MS) not covered / insufficiently covered by historical data. 

6.  Defining the audit trail for the most promising (data-wise) EU-level SCO in each considered sub-

area. 

7.  Consulting with stakeholders about the proposed EU-level SCOs and their audit trail within 

dedicated focus group discussions, also during presentations to members of the network of ERDF/CF 
SCO practitioners and meetings with other European Commission stakeholders. 

The development of SCOs is a data-driven exercise, therefore the availability and quality of data are paramount 
for  robust  calculations.  The  level  of  detail  and  completeness  of  these  data  varied  significantly  across  the 

1 OJ L 231/159 30.6.2021, p. 159.  

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

analysed areas. The most prominent shortcoming being its insufficient granularity, especially in the case of 
data on incurred costs: (a) only total costs were indicated; (b) their breakdown by activity and by category / 
type of costs was lacking; (c) only partial data on costs per activity was included (e.g. specified only for some 
types  of  activities).  Another  challenge  came  from  inconsistencies  in  the  data  on  results  and/or  outputs  of 
supported operations, i.e. different countries used different indicators and accordingly reported on different 
types of results / outputs produced in their projects. Lastly, the incongruent typology of activities in operations 
supported by different Member States made the cross-country comparison difficult. This in turn complicated a 
common definition of the operation for the proposed SCO.  

Data from alternative sources were also used sparingly mainly due to the insufficient level of granularity and 
detail.  To  mitigate  these  issues,  a  method  of  statistical  inference  (linear  extrapolation  using  Ordinary  Least 
Squares  –  OLS)  was  used  to  accommodate  Member  States  which  were  not  covered  /  partially  covered  by 
historical data. In a nutshell, the Member States for which unit cost rates were derived from historical data were 
then used to predict the missing unit cost values for all remaining Member States by using explanatory variables 
from Eurostat. Furthermore, to ensure that the SCO amounts / rates remain relevant in the future, the updates 
proposed are based on statistical indicators extracted from Eurostat. These can be used to update the established 
SCO rates / amounts on a regular basis.  

The result of the study is a mixture of EU-level solutions in the form of unit costs and lump sums. For sub-
area 2C, no SCO could be developed, since the collected data were very heterogeneous in terms of supported 
activities, cost categories and outputs.  

Area 

Sub-area 

Data availability 

Type  

Indicator 

(1) Energy 
Efficiency and 
Renewable Energy 

Energy efficiency and 
renewable energy in SMEs 
(1A) 

2 Member States (IT, 
PL)  

Unit 
cost 

Decrease of annual Greenhouse Gas (GHG) 
emissions, by tons of Carbon Dioxide (CO2) 

Energy efficiency in 
households and non-
residential public buildings 
(1B) 

12 Member States 
(IT, PL, GR, BG, CY, 
CZ, DE, ES, FR, LU, 
MT, PT) 

(2)  Research  and 
Innovation 
activities 

Knowledge / technology 
transfer activities of SMEs 
(2A) 

9 Member States 
(BE, HR, HU, FR, IT, 
PL, PT, LU, SI) 

Unit 
cost 

Full-time equivalent (FTE) gross new 
working positions (that did not exist before) 
to directly perform R&D activities in the 
SME, by monthly FTEs 

Innovation vouchers for SMEs 
for R&D implementation (2B) 

(3) SME Growth & 
Competitiveness 

Networking activities of SMEs 
(3A) 

11 Member States 
(CY, CZ, EE, FR, IT, 
LV, PL, PT, HR, HU, 
MT) 

8  Member  States 
(CZ,  ES,  FR,  HR,  IT, 
PL, PT, SI) 

Lump 
sum 

Innovation vouchers issued to an SME for 
completing innovation/digitisation 
activities, by units  

Lump 
sum 

SMEs attending international event, by 
units 

Consultancy / advisory 
services to SMEs for digital & 
green transformation (3B) 

9 Member States (BE, 
CZ,  ES,  HU,  IT,  PL, 
PT, SE, SI) 

Lump 
sum 

SMEs having elaborated an 
internationalisation strategy, by units 

Source: compiled by the study team. 

The study has also assessed the feasibility of developing EU-level result-based tools in three areas that were 
identified by the European Commission and Member States, namely promoting sustainable multimodal urban 
mobility  (FC1),  skills  for  smart  specialisation  and  transition  (FC2)  and  climate  change  adaptation  and  risk 
prevention (FC3). 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

Introduction 

A focus on results and simplification across all EU funding programmes have been a major goal of the European 
Commission for a number of years. In 2011, the European Commission launched an Agenda of simplification 
as part of the Multiannual Financial Framework (MFF) 2014-20202. The Agenda proposed reducing the number 
of  programmes,  enhancing  coherence  and  clarity  of  rules,  clarifying  priority  objectives,  using  simplified 
instruments  for  decision-making,  extending  simplified  forms  of  grants,  and  moving  towards  electronic 
governance. A few years later, the European Commission announced the EU Budget Focused on Results. The 
initiative aimed at maximising the effectiveness of the Union’s budget in supporting growth, jobs and stability 
in  Europe  and  beyond3.  It  encompassed  several  objectives  and  work  streams,  including  simplification, 
flexibility in implementation and lower administrative burden. This was followed by the amendment to the 
Financial  Regulation  in  2016,  which  sought  to  expand  the  use  of  targets  in  implementing  the  programmes 
funded  from  the  EU  budget.  In  spite  of  these  measures,  the  Reflection  Paper  on  the  Future  of  EU  Finances 
published by the European Commission in 20174 highlighted the need for further simplification and flexibility 
to enable more efficient spending and greater focus on performance. 

The increased focus on results over implementation procedures and tracking of the actual costs incurred was 
again extended in  the Omnibus  Regulation5 adopted in 2018. The latter  extended the use of simplified cost 
options in ESI Funds and  introduced a new form  of  financing that is not linked to the  costs  of the relevant 
operations but that is based instead on fulfilling conditions related to realising  progress in implementing or 
achieving programme objectives (i.e. ‘financing not linked to cost’). In 2018 also, the European Commission 
adopted its proposals for a new MFF for 2021-2027. The proposal for the Common Provisions Regulation (CPR)6 
contained  around  80  simplification  measures  for  the  Cohesion  Policy  2021-2027.  Bearing  in  mind  the 
recommendations of the High Level Group on Simplification for post 20207, it was planned to further simplify 
the management of Cohesion Policy through an increased use of simplified cost options and payments based 
on conditions.  

Simplified cost options (henceforth – SCOs) were originally introduced in the programming period 2007-2013 
and are widely recognised as one of the most effective simplification measures adopted under the Cohesion 
Policy8. This recognition comes from multiple working documents and evaluation / audit reports pointing to a 
number of benefits attributable to SCOs: 

•  First, the use of SCOs simplifies the funding application procedures and reporting requirements, 
and significantly reduces the administrative workload at application and implementation stages for 
all  actors  involved  in  the  management  and  implementation  of  the  EU  Cohesion  Policy.  This  is 

2 Commission Communication to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions on 
A Simplification Agenda for the MFF 2014–2020, COM(2012) 42. 

3 http://ec.europa.eu/budget/budget4results/index_en.cfm  

4 European Commission, The Reflection Paper on the Future of EU Finances, 2017. 

5 Regulation (EU, Euratom) No 2018/1046 of the European Parliament and of the Council of 18 July 2018 on the financial rules applicable to the general 
budget of the Union. 

6 COM(2018) 375 final – Proposal for a REGULATION OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL laying down common provisions on 
the European Regional Development Fund, the European Social Fund Plus, the Cohesion Fund, and the European Maritime and Fisheries Fund and financial 
rules for those and for the Asylum and Migration Fund, the Internal Security Fund and the Border Management and Visa Instrument. 

7 The High Level Expert Group on monitoring simplification for beneficiaries of ESI Funds, Final conclusions and recommendations of the High-Level Group on 
Simplification for post 2020, 2017. 

8 European Commission, Use of new provisions on simplification during the early implementation phase of ESIF, Final Report, SWECO, t33 & Spatial Foresight, 2017. 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

• 

achieved by eliminating the need for beneficiaries to prove their actual project costs to the managing 
authorities or intermediate bodies by providing them with respective invoices, proofs of payments, 
bank statements, etc. Instead, when SCOs are used, the tracing of every euro of co-financed expenditure 
to individual supporting documents is no longer required: this is the key point of simplified cost options as it 
significantly alleviates the administrative burden9. 
Second,  pre-established  and  published  SCOs  allow  beneficiaries  to  take  them  into  account  when 
planning  project  implementation  and  submitting  their  applications.  On  top  of  increased 
predictability,  the  pre-established  SCO  rates  contribute  to  transparency  and  equal  treatment  of 
beneficiaries,  and  also  provide  incentives  for  the  economic  use  of  resources.  In  addition,  less 
complex funding rules leads to various programme documents (e.g. guidelines for applicants, grant 
contracts, etc.) being clearer and more user-friendly, providing more organisations with access to 
funding and contributing to a better achievement of intended policy results. 

•  Third, SCOs allow managing authorities, intermediate bodies and beneficiaries to focus more on the 
achievement  of  policy  objectives  and  intervention  priorities  rather  than  being  concentrated  on 
collecting and verifying financial documents. Given that beneficiaries do not need to collect, store 
and  check  the  invoices,  proofs  of  payments,  bank  statements  and  other  documents,  they  have 
significantly more time to perform their primary work, i.e. planning and executing project activities. 
Similarly,  institutions  responsible  for  controlling  and  monitoring  the  programme  can  focus  on 
implementing the supported activities and on achieving expected results rather on justifying costs. 
Less  complex  control  procedures  also  allow  for  speedier  reimbursement  of  expenditure  to 
beneficiaries. 

•  Finally, the application of SCOs in different funds and programmes reduces error probability. The 
European Court of Auditors concluded that the projects whose costs are declared using simplified 
rules are less error prone if the conditions in place are not overly complex10. 

Overall, the use of SCOs positively benefits the financial and non-financial management of ESIF programmes 
at the level of beneficiaries, management bodies and the whole programme. A similar conclusion has been made 
in  the  study  carried  out  by  DG  REGIO  in  2018,  which  posited  that  ‘the  highest  potential  for  reductions  of 
administrative costs and burden lies with a massively increased uptake of SCOs, following an extension of their scope’11. 

The use of Financing Not Linked to Cost (henceforth – FNLC) tools was very limited in ESIF programmes in 
the period 2014-2020. Only Austria had used this results-based tool to support business investments in the field 
of renewable energy and energy efficiency12. A number of objective reasons limited the uptake of FNLC in 2014-
2020: insufficient time and resources to develop the scheme, as this innovative form of financing was introduced 
in  the  second  half  of  the  programming  period;  uncertainty  on  how  FNLC  tools  should  be  designed, 
implemented  and  audited;  difficulties  in  identifying  results  or  conditions  to  be  achieved  to  trigger 
reimbursements; administrative burden linked to designing FNLC tools13. These limitations, however, do not 
undermine the simplification potential and attractiveness of FNLC tools. The majority of the Member States are 
interested in using this form of financing in the programming period 2021-202714. 

9 European Commission, Guidance on Simplified Cost Options (SCOs): Flat rate financing, Standard scales of unit costs, Lump sums – Revised edition following the 
entry into force of Regulation (EU, Euratom) 2018/1046, 2021. 

10 European Court of Auditors, Annual report of the Court of Auditors on the implementation of the budget concerning the financial year 2018, 2019. 

11 European Commission, New assessment of ESIF administrative costs and burden, Final Report, SWECO, t33 & Spatial Foresight, 2017. 

12 Transnational Network (TN) of ERDF/CF SCO practitioners, Preliminary survey on “Financing not linked to cost” Key outcomes and responses from TN members  
(6th meeting of the TN – July 2020). 

13 Ibid. 

14 Ibid. 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

In  the  context  of  the  above-mentioned  policy  developments  and  inspired  by  a  clear  demand  for  further 
simplification  of  the  ERDF/CF  management,  the  Directorate-General  for  Regional  and  Urban  Policy  of  the 
European Commission commissioned this study. It is intended and aims to support the adoption of EU-level 
SCOs  (under  Art.  88(4)  CPR)  and  the  development  of  EU-level  FNLC  tools  under  the  ERDF/CF  during  the 
programming period 2021-2027. To this end, the study provides data-driven suggestions for developing EU-
level SCOs in the following policy areas: 

•  Area 1: Energy Efficiency and Renewable Energy, in particular actions / interventions for energy 
efficiency and renewable energy in SMEs (sub-area 1A), and energy efficiency and renewable energy 
in public and housing sector buildings (sub-area 1B); 

•  Area 2: Research and Innovation  activities, in particular  actions / interventions for  knowledge /  
technology transfer activities of SMEs (sub-area 2A), innovation vouchers for SMEs (sub-area 2B), 
and  research  development  and  innovation  research  projects  focusing  on  collaboration  between 
SMEs and public / private research institutions (sub-area 2C); 

•  Area 3: SME Growth & Competitiveness, in particular networking activities of SMEs (sub-area 3A) 
and consultancy / advisory services to SMEs for digital and green transformation (sub-area 3B). 

In addition, the study concludes on the feasibility of developing FNLC tools in three additional policy areas, 
namely (i)  sustainable  multimodal urban mobility, (ii) skills for  smart specialisation and transition, and (iii) 
climate change adaptation and risk prevention. 

The report is structured into three main chapters. Chapter 1 introduces the methodological approach adopted 
by the study team to collect, clean and analyse the underlying data used for both the development of proposed 
EU-level  SCOs  and  the  feasibility  assessment  of  potential  FNLC  tools.  Chapter  2  presents  the  main  study 
findings and selected study results, focusing on alternatives deemed the most appropriate and feasible for EU-
level SCOs and outcomes of the feasibility checks performed on potential EU-level result-based tools. Chapter 3 
summarises the end-result of analyses performed, and solutions recommended in each analysed area. Three 
annexes accompany the main study report and these can be found at the end of this document. 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

1.  Methodology 

The scope of the study includes the development of EU-level SCOs (Art.88(4) CPR) for each Member State in 
three  policy  areas  identified  namely  as  Energy  Efficiency  &  Renewable  Energy  (Area  1),  Research  and 
Innovation  activities  (Area  2),  and  SME  Growth  &  Competitiveness  (Area  3).  The  said  areas  &  actions  / 
interventions have been identified by Member States as relevant and feasible for developing EU-level SCOs. 
For our approach towards developing deliverables for this task, please see section 1.1. 

The study has also assessed the feasibility of developing EU-level result-based tools (i.e. FNLC) in three areas 
that  were  identified  by  the  European  Commission  (EC)  and  Member  States,  namely  promoting  sustainable 
multimodal urban mobility (Feasibility Check 1, FC1), skills for smart specialisation and transition (FC2) and 
climate change adaptation and risk prevention (FC3). For our approach towards this deliverable, please refer 
to section 1.2. 

1.1.  EU-level SCOs 

According to Article 94 of the Common Provisions Regulation15, Simplified Cost Options defining, at EU-level, 
unit costs, lump sums or flat rates, their amounts and adjustment methods should be based on the following: 

a)  a fair, equitable and verifiable calculation method based on any of the following: 

statistical data, other objective information or an expert judgement; 

• 
•  verified historical data; 
• 

the application of usual accounting practices; 

b)  draft budgets; 
c) 

the rules on corresponding unit costs, lump sums and flat rates applicable in Union policies for  a 
similar type of operation; 

d)  the rules on corresponding unit costs, lump sums and flat rates applied under schemes for grants 

funded entirely by the Member State for a similar type of operation. 

In this section, we present the overall methodology for this assignment and  the concrete steps taken: 

– 
– 

– 

– 

– 

first, we operationalise our overall methodological approach for Task 1 for Areas 1-3 (section 1.1.1); 
second, we explain what preparatory activities were taken in order to kickstart the SCO development 
process (section 1.1.2); 
third,  we  provide  an  overview  of  the  data  collection  process,  its  results  and  our  approach  towards 
cleaning the data received from Member States (section 1.1.3); 
fourth, we briefly touch upon the feasibility assessment undertaken in order to shortlist only the most 
feasible SCO options for further development (section Error! Reference source not found.); 
fifth, we outline our methodological approach to the statistical analysis of the collected data, including 
the method to extrapolate  costs for those EU Member States that may lack complete and robust data 
(section 1.1.5); 

15 https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32021R1060  

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

– 

lastly,  we  indicate  what  stakeholder  consultations  were  undertaken  throughout  the  duration  of  the 
study and what was their purpose (section 1.1.6). 

1.1.1.  Design 

The  approach  to  development  of  EU-level  SCOs  adopted  in  this  study  is  based  on  a  tried  and  tested 
methodology  utilised  in  multiple  studies  related  to  the  development  of  SCOs.  It  consists  of  seven  strongly 
interlinked  and  highly  complementary  steps  –  this  approach  is  illustrated  in  Error!  Reference  source  not 
found.. 

Figure 1. SCO development process 

Source: prepared by the study team. 

Step 1: Preparatory activities (structuring). This step is dedicated to developing the data collection tools and 
preparing to launch the full-scale data collection exercise. In turn, this step is also crucial for anticipating and 
managing the potential challenges behind the historical data collection in this assignment.  

Step 2: Collecting historical data on the selected interventions (funded by ERDF and CF; and/or national and 
regional  sources,  to  the  extent  they  would  be  eligible  under  ERDF/CF)  from  EU  Member  States.  This  data 
collection  exercise  covers  all  27  EU  Member  States.  The  data  collection  covers  data  at  the  level  of  ‘call  for 
proposals’  and  projects.  As  explained  under  Step  4,  if  information  on  actual  costs  and  outputs  or  inputs  is 
difficult  to  obtain  or  unavailable  altogether  in  some  Member  States,  the  incomplete  historical  data  may 
sometimes be replaced by / complemented / enriched by the data collected from alternative sources (see Step 
3). If, however, comparable proxy data are unavailable, only historical data collected from Member States  will 
be used to extrapolate (i.e. to infer from values within an interval observed in the sample) the SCO amounts / 
rates to Member States that did not provide such data.  

Step 3: Collecting (proxy) data from alternative sources. Step 3 complements and expands the scope of data 
collection  activities  carried  out  under  Step  2.  It  seeks  to  map  and  collect  other  data  (e.g.  relevant  studies, 
statistical databases, market survey data, etc.) which may prove useful for filling in the gaps in the historical 
data  collected  from  Member  States,  comparing  the  SCO  amounts  /  rates  when  they  can  be  established  by 

16 

 
 
Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

utilising data from multiple sources and informing the extrapolation of SCO amounts / rates for Member States 
otherwise  not  covered  by  collected  (historical)  data.  The  exact  purpose  and  utility  of  (proxy)  data  collected 
during this complementary step depends on the quality of historical data collected in Step 2. In extreme cases 
(e.g. when a historical data sample is very small or when collected historical data are of very low quality), proxy 
data may even be used as the main source for calculating the SCO amounts / rates, including extrapolation of 
SCO amounts / rates for Member States not covered by this proxy data.  

Step 4: Interpreting and analysing the collected data. This step involves constant checking of collected data to 
ensure its consistency, comparability, and robustness. It is implemented largely in parallel to Steps 2 and 3. 
Depending on data quality and availability, it may result in an additional effort to collect missing or additional 
data from Member States. During this step we also consider the evident synergies of historical and (proxy) data 
collected in Steps 2 and 3. For example, if historical data for some areas of activity or for certain Member States 
were of insufficient quality, we check whether merging of historical and proxy data are an option for building 
a more complete sample. This step involves conducting a comprehensive feasibility analysis of possible SCO 
options.  

Step 5: Applying and elaborating methodologies for calculating SCO rates and amounts. Step 5 is used to 
further elaborate and specify the calculation method that has been applied in the study to estimate the proposed 
unit  cost  rates  or  lump  sum  amounts.  If  multiple  alternatives  (e.g.  both  input-based  and  output-based  unit 
costs) are deemed feasible for development of an EU-level SCO in the analysed area, a selection of proposed 
calculation  methods  and  their  outcomes  are  presented  to  the  client.  Among  other  things,  this  step  includes 
i) proposing a method to extrapolate SCO rates for Member States not covered or incompletely covered by the 
sample of historical and/or proxy data; ii) defining a detailed scope (categories of costs, types of activities and/or 
operations)  of  each  SCO  alternative  proposed  for  further  consideration;  iii)  agreeing  on  the  preferred  SCO 
alternative(s) with the Directorate-General for Regional and Urban Policy (DG REGIO); and iv) reporting on 
calculated rates and providing a detailed description of each established SCO, including the proposed method 
for their adjustment in the future. 

Step 6: Defining and testing the audit trail. This step is usually implemented in parallel with Step 5. It consists 
of analytical work to define the specific audit trail arrangements for each EU-level SCO alternative proposed 
by the  study, and their  validation  with the  Managing Authorities (MAs). This step is required to ensure an 
effective  and  sufficient  documentation  of  quantified  inputs,  outputs  or  results  of  operations  covered  by  the 
proposed EU-level SCOs. 

Step  7:  Consulting  with  stakeholders.  This  step  is  designed  to  gather  inputs  and  feedback  from  key 
stakeholders within Members States, namely Managing Authorities, Intermediary Bodies (IBs), line ministries 
responsible for policy areas relevant for this study and members of the network of ERDF/CF SCO practitioners). 
In addition, other stakeholders within the European Commission have also been closely involved in the final 
definition and structure of the proposed EU-level SCOs. This step took place during several stages of the SCO 
development process:  

•  Contribution to structuring phase (Step 1) by piloting the draft data collection form and giving initial 

feedback on its suitability and effectiveness for collection of historical data. 

•  Data collection phase (Step 2) involves approach Member States on historical data on relevant calls 
/ projects from ERDF/CF. As historical data are the primary source of data used in this study, it was 
crucial to involve National Experts in order to support and engage data owners to provide the study 
team the necessary data to conduct further SCO calculations. 

•  Member States have been asked to help the study team when interpreting collected data (Step 4). 
As data collection is an iterative process with multiple steps, checks and follow-ups with Member 

17 

 
Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

States were facilitated in order to collect and validate the most comprehensive sample of historical 
data possible. 

•  Member States also provided feedback and their suggestions on the possible approach to the audit 

trail (Step 6) during several focus groups conducted for all three Areas. 

Lastly, stakeholders were also closely consulted on the study’s outputs, caveats and potential impact to ensure 
that  the  developed  SCOs  are  aligned  with  the  needs  and  priorities  of  Member  States  and  key  stakeholders 
within the European Commission. Overall, the step should be seen as an ongoing feedback loop throughout 
the duration of the study. This enables the final product to be properly disseminated, discussed and deliberated 
upon in various formats (bilateral or multilateral). 

1.1.2.  Preparatory activities 

This step is dedicated to developing the data collection tools and preparing for the launch of the full-scale data 
collection  exercise.  In  turn,  this  step  is  also  crucial  for  anticipating  and  managing  the  potential  challenges 
behind the historical data collection in this assignment. 

The EU-level SCOs in the area of Energy Efficiency and Renewable Energy (Area 1), Research and Innovation 
activities  (Area  2)  and  SME  Growth  and  Competitiveness  (Area  3)  presented  in  this  report  are  accordingly 
drawing  on  analysis  of  verified  historical  data  collected  at  national  and/or  regional  levels  and  statistical 
inference where such data are insufficient or unavailable. A comprehensive set of preparatory steps had to be 
taken to collect the historical data enabling such analysis: 

1.  exploratory research of the underlying interventions was carried out to collect information on their key 
constituent parts: activities and cost items typical in implemented projects, indicators used to track their 
progress and overall performance in terms of produced outputs and/or results, etc; 

2.  a  comprehensive  structured  grid  /  data  collection  form  (DCF)  and  accompanying  guidelines  were 

developed for collecting historical data from the Member States; 

3.  stakeholder consultations were organised to test the fit-for-purpose of developed DCF and collect 
more in-depth information on relevant interventions. This was done through piloting the draft data 
collection forms with the pre-selected Managing Authorities and focus group discussions with the 
Managing Authorities and Implementing Bodies from all 27 Member States. 

In the case of Area 1, preparations for the data collection process ended in late May 2022, immediately after the 
sample  of  74  Operational  Programmes  selected  for  historical  data  collection  had  been  agreed  upon  and 
approved by the Commission. The study team used the stratified disproportional sampling method to select 
this sample from the sampling frame of 137 OPs. The parameters used for the sampling frame are presented in 
Box 1. 

Box 1. Sampling frame conditions  

Investment in priorities contributing to energy efficiency and renewable energy 

OPs  must  invest  in  interventions  under  Thematic  Objective  4  –  Low-carbon  economy16  and  cover  the 
following investment priorities: 

a)  promoting the production and distribution of energy derived from renewable sources; 

16 https://ec.europa.eu/regional_policy/en/atlas/programmes  

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

b)  promoting energy efficiency and renewable energy use in enterprises; 
c)  supporting energy efficiency, smart energy management and renewable energy use in public 

infrastructure, including in public buildings, and in the housing sector. 

Application of common ERDF/CF indicators 

OPs  collect  information  on  outputs  or  results  achieved  in  supported  interventions  using  the  following 
common ERDF/CF indicators: 

•  CO30 Additional capacity of renewable energy production, measured in MW 
•  CO31  Number  of  households  with  improved  energy  consumption  classification,  measured  in 

households 

•  CO32  Decrease  of  annual  primary  energy  consumption  of  public  buildings,  measured  in 

kWh/year 

•  CO34 Estimated annual decrease of GHG, in Tons of CO2 equivalent, measured in tons of CO2 

equivalent 

This criterion ensures that historical data on achieved outputs and/or results collected from sampled OPs is 
comparable across the EU Member States. An EU-level SCO cannot be developed if the collected cost data 
cannot be linked to the same type of output or result in all EU Member States providing such data. 

Tracking of common ERDF/CF indicators 

OPs monitor the progress made against the common ERDF/CF indicators listed above, i.e. their values are 
>0. This information was cross-referenced with data from the annual implementation reports. 

Please note that the sampling frame was retained for Areas 2 and 3, with only slight amendments to account 
for those OPs which were not relevant in the case of Areas 2 and 3 (i.e. those which did not fund any relevant 
operations). This decision was also taken in order to utilise already existing contacts within the respective OPs, 
thus ensuring both the timeliness and quality (as a result of the familiarity with the data collection exercise) of 
the provided data. Please refer to Annex 3 where we elaborate upon the details of the historical data collection 
process and specify how effective it has been in terms of the Operational Programmes covered.  

The primary goal of the data collection process was to collect  historical data on interventions related to  the 
following sub-areas:  

•  Energy Efficiency and Renewable Energy (Area 1) 

o 

o 

o 

implementation of energy efficiency and renewable energy measures in small or medium 
enterprises (sub-area 1A); 
implementation of energy efficiency and renewable energy measures in housing sector 
(sub-area 1B.1); 
implementation of energy efficiency and renewable energy measures in non-residential 
(public) sector (sub-area 1B.2). 

•  Research and Innovation activities (Area 2) 

o 
o 
o 

implementation of knowledge / technology transfer activities of SMEs (sub-area 2A); 
implementation of innovation vouchers for SMEs for R&D implementation (sub-area 2B); 
implementation of Research Development & Innovation research projects (sub-area 2C). 

•  SME Growth & Competitiveness (Area 3) 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

o 
o 

implementation of networking activities of SMEs (sub-area 3A); 
implementation of consultancy / advisory services to SMEs for digital & green 
transformation (sub-area 3B). 

Please note that sub-areas 1B.1 (housing) and 1B.2 (non-residential public buildings) have been merged together 
into  a  single  sub-area  1B  in  later  stages  of  the  study.  As  a  result,  the  outcomes  from  the  SCO  development 
process are presented together in this report in Chapter 2. This was done as there were concerns related to the 
solidity of the historical data collected from the Member States. In particular, due to data limitations, few unit 
cost  rates  were  derived from  historical  data,  with  the  rest  coming  from  extrapolations.  To  mitigate  this,  the 
study team proposed to merge the two sub-areas based on the following rationale: 

–  Both sub-areas share an identical set of activities as part of relevant operations. 
–  SCO rates initially calculated for 1B.1 and 1B.2 were very similar for the majority of eligible activities. 

A larger combined sample of historical data is now being used to establish a single unit cost rate per Member 
State for Energy Efficiency (EE) and Renewable Energy Sources (RES) operations in housing and non-residential 
buildings sectors. As a result, rates had to be revised for the new sub-area 1B. The key reasons for these changes: 

–  Merging data from the two sub-areas tends to favour the larger historical sample coming from either 
housing or non-residential buildings, thus changing the values calculated based on historical data.  
–  Outlier analysis was redone based on the merged dataset, so certain projects may be included in the 

calculations or vice versa, thus changing the values calculated based on historical data. 

The data collection process was guided and structured by tailored data collection forms developed specifically 
for this assignment. The forms asked the MAs and IBs responsible for sampled OPs to provide the following 
information: 

• 
• 

source of funding, i.e. whether ERDF, CF or national funds had been used to finance the projects; 
type of operation, i.e. energy efficiency and renewable energy measures; research and innovation 
measures; measures linked to SME growth and competitiveness; 

•  any national or regional level SCOs in this area and their application in the sampled projects; 
• 
type of data provided at the request of the study team, i.e. if the data in question are provided 
aggregated at the level of calls for proposals or disaggregated at the level of individual projects; 
reference information, i.e. number, title, period of calls / projects; 

• 
•  activities supported, including the activities listed in the data collection form (see Table 1 for the 

typologies of activities in all sub-areas). 

•  Data on all direct17 and indirect18 costs covered in the sampled projects broken down by category 

for each type of measure; 
results achieved and/or outputs produced, focusing primarily on results / outputs measured by 
common ERDF/CF indicators;  
any additional information in the form of comments and/or clarifications. 

• 

• 

17 These are costs which can be attached exclusively to a single service or product (in the context of SCO, this service is usually an activity). 
18 These are general administrative costs – overhead costs incurred in connection with the eligible direct costs for the action. 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

Table 1. Typologies of activities included in data collection forms 

SUB-AREA  

TYPOLOGIES 

Energy efficiency and 
renewable energy in 
SMEs (1A) 

Energy efficiency in 
households and non-
residential public 
buildings (1B) 

Knowledge/technology 
transfer activities of 
SMEs (2A) 

Innovation vouchers 
for SMEs for R&D 
implementation (2B) 

•  Advice consultancy to SME owners on energy efficiency and potential for 

• 

renewable energy 
Energy audits to identify, quantify and report existing energy consumption 
profiles and energy savings opportunities for SMEs 
•  Replacement of window frames / glass / shading systems 
•  Upgrade of thermal insulation 
•  Upgrade of heating / cooling systems (including based on RES) 
• 
• 
• 
• 
•  Consultancy service for preparing the necessary technical studies / reports 

Installation of hot water system with the use of RES 
Installation of photovoltaic system 
Energy storage systems 
Smart management systems  

as well as monitoring the implementation of the interventions 
Energy efficiency upgrade of production equipment for groups of SMEs 

• 

Energy audits for buildings 

• 
•  Replacement of window frames / glass / moving of fixed shading systems 
•  Upgrade of thermal insulation (walls, roofs, ceiling etc.) 
• 

Installation of new high efficiency or upgrade of existing heating / cooling 
systems (including based on RES) 
Installation of hot water system with the use of RES 
Installation of renewable electricity unit (e.g. photovoltaic system / heat 
pump) 
Energy storage systems 
Smart management systems (including automation and control system and 
remote smart meters) 

• 
• 

• 
• 

•  Consultancy service for preparing the necessary technical studies / reports 
as well as monitoring the implementation of the interventions (project 
management / supervision works) 
Energy efficiency upgrade of healthcare facilities  
Energy efficiency upgrade of public non-residential buildings (schools, 
administrative, courthouse, etc.) 

• 
• 

• 
• 

• 
• 

Intersectoral mobility activities 
Support measures for creating spin-offs to increase the entrepreneurial 
capacity of research organisations and promote the translation of research 
results into economic value 
Post-doctoral researcher placements in the private sector, including SMEs 
Support for industrial PhDs and traineeships 

•  Consultancy services provided to SMEs by universities, research centres or 

knowledge-intensive companies 
•  Access to research centres and facilities 
• 

Establishing and sustaining contacts between SMEs and research facilities, 
nationally or internationally 
• 
Education and training activities 
•  Development of digital capabilities 
• 

Investment in Information and Communications Technology (ICT) 
instruments 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

SUB-AREA  

TYPOLOGIES 

Networking activities 
of SMEs (3A) 

Consultancy/advisory 
services to SMEs for 
digital & green 
transformation (3B) 

Source: prepared by the study team. 

• 
• 
• 

Participation in trade fair 
Partnership exchange 
Participation in international scientific conferences 

•  Market research 
• 
Export strategy 
•  Capacity building actions 

The  study  team  did  not  pre-fill  activities  for  sub-area  2C  (Research  Development  &  Innovation  research 
projects)  because  desk  research  and  piloting  demonstrated  that  the  activities  in  this  sub-area  were  very 
heterogenous  across  the  Member  States  and  selected  OPs.  Historical  data  collection  confirmed  this 
assumption since activities varied significantly under this sub-area. Some of the most common examples of 
activities in this area included industrial research and experimental development, feasibility studies, project 
management and consulting. For sub-area 3B, information on activities supported in projects, including their 
type, number and duration. 

1.1.3.  Data collection, cleaning and preparation for the analysis 

The process of data collection for Area 1 was launched in early June 2022, with our national experts reaching 
out to authorities responsible  for  the  74 Operational Programmes sampled for  data collection in Area 1.  To 
facilitate the data collection process, the study team had the data collection form for each Member State pre-
filled with information on (potentially) relevant ERDF/CF projects. The pre-filled information included titles of 
relevant calls for proposals / projects and their status (i.e. if relevant projects are completed or still ongoing). 

By early September 2022, 24 Member States had responded to the study team’s requests by providing historical 
data for 72 out of 74 sampled OPs. The data collection process for Areas 2 and 3 was launched in July 2022, 
with our national experts reaching out to authorities responsible for 74 OPs sampled for data collection in Areas 
2 and 3.  

Notably, the requested historical data were often unavailable from central management systems and had to be 
collected at the level of IBs, in some cases directly from project documentation. To this end, as indicated above, 
a pool of experienced national experts had been engaged to ensure that relevant authorities  were supported 
throughout  the  data  collection  process.  These  experts  were  responsible  for  disseminating  the  DCFs  to 
representatives of the MAs and IBs in each Member State, aiding them in the process of the DCF population, 
and maintaining a constant communication throughout. 

Since  data  collection  is  an  iterative  process,  the  study  team  performed  a  rigorous  data  quality  check  to 
determine  its  suitability  for  developing  EU-level  SCOs  or  other  tools.  In  cases  where  some  data  were 
unavailable  or  unclear,  the  study  team  organised  follow-ups  with  the  Member  States  to  confirm  data 
availability, potentially fill out any data gaps or interpret the previously collected data. In addition, the study 
team was unable to follow-up with some Member States that submitted significantly later than the deadline.  

The study team also explored why some Member States could not provide data for certain sub-areas or data 
points. Some of the common responses were that: a) Member States did not implement relevant interventions; 
b) In some cases, even if relevant interventions were implemented, data could not be extracted from the projects 
since it was not monitored to the requested detail due to different reporting requirements; c) In certain cases, 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

where  the  Member  States included ongoing projects and/or  calls, the  information about outputs and results 
was not provided since it wasn't finalised and reported yet.  

As part of the historical data cleaning process, outliers were iteratively removed from the sample to ensure 
that  only  high-quality  and  reliable  data  were  included  in  the  calculations.  The  study  team  conducted 
interquartile analysis (see Box 2 for details) and applied expert judgement to reliably identify the outliers and 
justify their exclusion from further analysis. 

Box 2. Approach to interquartile analysis for the purpose of removing outliers 

A quartile is a statistical division of a dataset into four equal groups, with each group making up 25 % of the data. The 
top 25 % of a collection is considered the 1st quartile, while the bottom 25 % is considered the 4th quartile. They can be 
calculated as follows: 

  Calculating the interquartile range for the data by subtracting the third quartile value from the first quartile 

value; 

𝐼𝑄𝑅 =  𝑄3 –  𝑄1 

  Multiplying the interquartile range by 1.5 (a constant used to discern outliers); 

  Add 1.5 x (IQR) to the third quartile. This is the upper bound – any number greater than this is a suspected 

outlier; 

𝐼𝑄𝑅 ∗  1.5 

𝑈𝑝𝑝𝑒𝑟 𝑏𝑜𝑢𝑛𝑑 =  𝑄3 +  1.5 ∗ 𝐼𝑄𝑅 

  Subtract 1.5 x (IQR) from the first quartile. This is the lower bound – any number less than this is a suspected 

outlier. 

𝐿𝑜𝑤𝑒𝑟 𝑏𝑜𝑢𝑛𝑑 =  𝑄3 −  1.5 ∗ 𝐼𝑄𝑅 

Notably, the interquartile rule is only a rule of thumb that generally holds but does not apply to every case in 
the quantitative analysis. Therefore, the study team analysed each outlier obtained by the interquartile method 
in the context of the entire dataset and, where a clear choice was not evident, expert judgement was applied. 
This type of outlier analysis is typically used whenever statistical modelling is insufficient, and when expert 
judgement  may  provide  reassurance  with  respect  to  resilience  and  accuracy19.  Such  outlier  cases  may  occur 
when the  quantitative sample  is low enough for  the  statistical methods to be ineffective. An example  of the 
situation where expert judgement may be applied can be found in Box 3 – both instances in the example would 
be qualified as outliers, as they contain insufficient qualitative information to explain why reported amounts 
are so low or so high. As suggested, outliers may also occur between Member States, rather than within Member 
States. 

19 Bellini, T., (2019) in IFRS 9 and CECL Credit Risk Modelling and Validation. 

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Box 3. Hypothetical scenario for outlier analysis based on expert judgement 

A relatively low amount of EUR 1000 was spent per one advisory consultancy for SMEs in several projects 
implemented in Member State A (a high living cost country). Information on the scope, duration and other aspects 
of this activity are not provided.  

A relatively high amount of EUR 10,000 was spent per one advisory consultancy for SMEs in several projects 
implemented in Member State B (a low living cost country). Information on the scope, duration and other aspects of 
this activity are not provided. 

A  detailed  description  of  the  sample  interventions  /  operations  used  to  establish  SCOs,  as  well  as  the  data 
cleaning techniques and unit cost calculation methods applied in this study, are provided in Chapter 2 of this 
report. 

1.1.4.  Feasibility analysis of potential SCO alternatives 

The development of EU-level SCOs is primarily a data-driven exercise. Therefore, the study team conducted a 
comprehensive  feasibility  assessment  of  the  quality  of  data  utilised  to  calculate  each  of  the  proposed  SCO 
alternatives. To enable an informed selection of the SCO alternatives which were shortlisted for further analysis 
in  the  study,  it  was  necessary  to  carry  out  a  rigorous  assessment  of  their  feasibility,  in  liaison  with  the 
Commission. To this end, we have assessed each SCO alternative against the following set of criteria: 1) data 
availability, 2) data granularity, and 3) data reliability. For a more comprehensive assessment of data quality 
for all areas, please refer to Annex 3 of the report. 

The exploring potential alternatives (per each area covered under Task 1) were then compiled considering the 
feasibility assessment mentioned earlier and other conceptual aspects of SCO development such as whether: 

• 
• 
• 

the proposed indicator is commonly used throughout Member States (common ERDF/CF indicator); 
the proposed indicator is result-based, output-based or input-based / process-based SCOs; 
the proposed indicator is a unit cost or a lump sum. 

For a more comprehensive overview of the feasibility analysis done of potential SCO alternatives, please refer 
to Chapter 2 of the report. 

1.1.5.  Calculation and adjustment methods 

The calculation process of the SCO options shortlisted for development consisted of the following:  

•  developing approaches and main unit cost estimation methods for SCOs;  
• 

conducting exploratory analysis to test the feasibility of different methods for establishing off-the-
shelf solutions; 
calculating SCO amounts for each Member State based on the historical data sample collected; 
adjusting SCO amounts to 2021 price levels based on inflation indices; and 
adjusting SCO amounts to include eligible indirect costs based on Common Provisions Regulation 
flat rates. 

• 
• 
• 

For more details on the precise calculation method used for each SCO, please refer to Chapter 2 of the report. 

For  those  Member  States  that  did  not  provide  us  with  (sufficient)  data,  a  method  of  statistical  inference 
(extrapolation) was used to  establish the rates / amounts for each proposed EU-level SCO. In a nutshell,  we 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

took  the  unit  cost  rates  and/or  lump  sum  amounts  calculated  for  those  Member  States  that  provided  the 
study  with  relevant  historical  data  and  used  it  to  predict  the  missing  rates  /  amounts  for  all  remaining 
Member States. Extrapolation as a tool to overcome the historical data quality issues was also applied in similar 
studies the study team has carried out to develop SCOs at EU-level. 

The extrapolation process was conducted by calculating proxy rates from Member States with data on a full or 
partial  set  of  activities  and  then  applying  a  linear  regression  model,  with  selected  statistical  indicators  as 
explanatory variables, to predict the proxy rates for countries without sufficient historical data.  

In sub-area 1A, a bivariate extrapolation model was used by utilising the cost index as the explanatory variable, 
followed up by with further indexation of inferred estimates based on the results index. This is because the size 
of the base historical data sample is too small to utilise a multivariate approach. For example, if the multivariate 
model is applied in the case of 1A, it will not return the parameter estimates (e.g. degrees of freedom, r-squared, 
f-statistic, p-value etc.) needed to ensure that the model works in the intended manner. For more details on the 
exact method, please see section Error! Reference source not found.. 

In sub-area 1B, the extrapolation was based on a multivariate linear regression model with the cost index and 
result index as explanatory variables. A similar approach was also taken when deriving rates for sub-area 2A. 
For more details on the exact methods used in these areas, please see section 2.1.2 and section 2.2.1 respectively. 

In sub-areas 2B, 3A and 3B, the extrapolation measures taken were done at the level of operation. This was done 
in  order  to  account  for  the  different  type  of  SCO  utilised  (lump  sum)  which  does  not  lend  itself  well  to 
disaggregation at activity level. For more details on the exact methods used in these areas, please see sections 
2.2.2, 2.3.1 and 2.3.2 respectively. Table 2 indicates the statistical indicators we propose to serve as explanatory 
variables of cost variation in Member States.  

Table 2. Proposed explanatory indicators for areas of the study 

AREA  

STATISTICAL INDICATOR 

RATIONALE 

Energy 
Efficiency and 
Renewable 
Energy 

• 

the composite cost index drawn 
from: 

o  Price Level Index (PLI) (EU-
27_2020=100) for machinery 
and equipment 

o  Labour cost for Labour Cost 

Index (LCI) (compensation of 
employees plus taxes minus 
subsidies) (EU-27 = 100), 
industry, construction and 
services (except public 
administration, defence, 
compulsory social security) 

• 

the composite results index based 
on the overall energy efficiency 
score of each EU Member State in 
2021 taken from the ‘EU energy 
efficiency scoreboard’ developed in 
the framework of the Horizon 2020 

The selected indicators are deemed relevant 
for predicting and explaining the missing 

values (i.e. unit cost rates for Member States 
not covered / partially covered by historical 

data) because: 

- 

they consider if the price level for 

- 

- 

equipment and machinery in one 
Member State is higher or lower 

than equipment and machinery 
in another Member State; 

they consider the extent to which 
labour costs in the specific 

economic activity are different 
across Member States; 
they consider the potential CO2 
savings that each Member State 
will be able to generate, based on 
the energy efficiency level (i.e. 
baseline situation), energy 
efficiency progress (i.e. energy 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

AREA  

STATISTICAL INDICATOR 

RATIONALE 

project Odyssee Mure20 which 
quantifies the following aspects: 

o 
o 

o 

the energy efficiency level; 
the energy efficiency 
progress (i.e. energy 
efficiency trends); 
the energy efficiency 
policies. 

Labour cost for LCI (compensation 
of employees plus taxes minus 
subsidies) in professional, scientific 
and technical activities (EU-27_2020 
= 100) (Eurostat, LC_LCI_LEV).  
Price level index for 
communication, machinery and 
equipment, and software 
(EU27_2020 = 1) (Eurostat, 
PRC_PPP_IND). 

Labour cost for LCI (compensation 
of employees plus taxes minus 
subsidies) in business economy 
(EU-27_2020 = 100) (Eurostat, 
LC_LCI_LEV).  

• 

• 

• 

efficiency trends) and energy 
efficiency policies (quantitative 
impacts collected from energy 
efficiency evaluations, based on 
energy savings in the past) in 
different Member States.  

The two indicators are deemed a relevant 
predictor of missing values for Member 
States not covered / partially covered by 
historical data as they consider the extent 
to which labour costs and price levels in 
the selected economic activities differ 
across Member States.  

This indicator best reflects the differences 
between Member States in a key cost item 
within the area – staff costs – which is prone 
to significant variation. The economic activity 
‘business economy’ most closely reflects the 
operations within this area. 

Research and 
Innovation 
activities 

SME Growth 
and 
Competitiveness  

Source: prepared by the study team. 

To ensure that the amounts / rates of developed EU-level SCOs retain their relevance in the future, the study 
team also proposed how these amounts / rates should be adjusted. To this end, we identified a set of annually 
updated statistical indicators tracked by Eurostat (Table 3). These indicators are considered to best reflect the 
inflation affecting operations in Areas 1, 2 and 3. 

Table 3. Proposed inflation indicators for areas of the study 

AREA  

STATISTICAL INDICATOR 

RATIONALE 

Energy 
Efficiency and 
Renewable 
Energy 

Construction cost index (or producer prices), 
new residential buildings. Percentage change 
compared to same period in previous year 
(Eurostat, STS_COPI_A) 

The study team reviewed indicators which 
would encompass both the labour costs and 
material costs – a single, multi-faceted indicator 
would allow for easier adjustment of SCO 
values and address the fluctuating costs of the 
two main cost categories seen in Area 1.  

The proposed indicator is relevant for adjusting 
the SCO values in both sub-areas. The objective 
of the construction cost index is to show the 
development of costs incurred by the contractor 
to carry out the construction process. 

20 https://www.odyssee-mure.eu/data-tools/scoring-efficiency-countries.html  

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AREA  

STATISTICAL INDICATOR 

RATIONALE 

Construction costs may be used if the reporting 
country is not compiling producer prices of new 
residential buildings. The construction cost 
index shows the price developments of 
production factors used in the construction 
industry. The index encompasses both material 
costs and labour costs. The material costs are 
generally calculated using material prices. Here, 
prices of materials are based on actual prices 
rather than list prices. Furthermore, prices are 
based on a sample of products and suppliers. 
The labour costs cover wages and salaries and 
social security charges for all persons employed. 

The proposed indicators are relevant for 
adjusting the SCO values in both sub-areas as 
they consider price developments of labour in 
selected sectors for each country compared to 
the previous period, as well as consumer price 
inflation in the euro area. 

The proposed indicator is relevant for adjusting 
the SCO values in both sub-areas as it considers 
consumer price inflation in the euro area. We 
utilise the general index because given the 
nature of 3A and 3B operations, none of the 
specific items of the provided index contain 
additional information which would help 
explain the variation of prices for this operation. 

Research and 
Innovation 
activities 

Labour cost index in professional, scientific 
and technical activities (Eurostat, 
LC_LCI_R2_A). Percentage change 
compared to previous year. 

Harmonised Index of Consumer Prices 
(HICP) in industrial goods, services and 
communication (Eurostat, 
PRC_HICP_AIND). Annual average rate of 
change. 

SME Growth 
and 
Competitiveness  

Harmonised Index of Consumer Prices. 
Percentage change compared to previous 
year. 

Source: prepared by the study team. 

1.1.6.  Consultations to test and validate study outcomes 

The study team conducted an extensive stakeholder consultation programme at separate stages of the study. 
The main objectives of these consultations were the following: 

introducing key stakeholders to the study, its objectives and desired outcomes; 

• 
•  piloting data collection forms before finalising them and disseminating to all Member States; 
• 
• 

informing stakeholders on the key parameters of proposed SCOs; 
testing and verifying if the definition of SCOs (activities covered) is accurate, sufficient and applicable 
for their intended purpose; 
testing and verifying if the audit trail arrangements and requirements are appropriate, fit-for-purpose 
and manageable; 
consulting on the potential value of the approach considered for SCO value estimates in the Member 
States not covered by historical data; 
addressing any arising caveats for the implementation of proposed SCOs. 

• 

• 

• 

The stakeholder consultation programme included the following forums: 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

•  bilateral consultations with Member States in order to pilot data collection forms; 
• 
•  presentations of study results and Question & Answer (Q&A) sessions during meetings with the 

focus group discussions with representatives from Managing Authorities and Intermediary Bodies; 

Transnational Network of ERDF/CF SCO practitioners; 

•  meetings with the Steering Group of the study; 
• 

consultations with the Directorate-General for Energy (DG ENER). 

The first presentation to members of the Transnational Network of ERDF/CF SCO practitioners was meant 
to introduce the study and the envisaged role of stakeholders within Member States. Participants were invited 
to  discuss  in  groups  on  the  next  steps  of  the  study  and  to  provide  comments  and  suggestions  for  their 
implementation. Specifically, participants were asked to indicate the issues that the study team might face when 
collecting historical data and the potential mitigation measures and suggestions to the study team on how to 
tackle identified issues. Stakeholders also raised questions regarding the data collection process.  

The draft data entry forms had been tested on a small sample of Member States as part of the piloting exercise 
during the Inception phase of the study. The study team approached and collected feedback on this tool from 
Italy, Lithuania, Malta, Poland, Portugal and Spain. The piloting exercise was carried out in order to: 

• 

check if the forms are sufficiently comprehensive for their intended purpose and to see how they 
could be improved, as well as to improve our team’s understanding of analysed interventions;  
assess the availability and quality of historical data;  

• 
•  obtain initial insights on effort required on behalf the Managing Authorities and National Experts 

to collect and provide all requested data. 

The first focus group discussion was organised in order to test our data collection tools and collect feedback 
from  a  larger  group  of  stakeholders.  The  focus  groups  involved  the  responding  competent  Managing 
Authorities and Implementing Bodies from the 27 EU Member States The findings that emerged during these 
meetings helped verify the clarity of the draft data collection forms, ensuring their uniform understanding as 
well as the relevance and coherence of the data which would be collected.  

The  second  focus  group  discussion  was  organised  in  order  to  validate  the  proposed  SCO  alternatives  and 
inform  stakeholders  on  the  development  of  EU-level  SCOs  for  ERDF/CF  in  the  area  of  Energy  Efficiency  & 
Renewable  Energy.  The  focus  group  discussion  revolved  around  the  discontinued  modular  SCOs  and  the 
prospective audit trail of the proposed SCO alternatives. 

The  study  team  presented  the  results  of  Area  1  in  the  second  meeting  with  the Transnational  Network  of 
ERDF/CF SCO practitioners. Here, the stakeholders were presented with further details of the proposed SCOs, 
including  the  development  steps,  the  reasons  for  selecting  the  indicator  and  the  calculation  method  of  the 
proposed  SCOs.  In  the  Q&A  after  the  presentation,  feedback  was  given  on  the  proposed  modular  SCO 
approach,  which  Member  States  were  satisfied  with.  The  stakeholders  shared  a  similar  sentiment  to  the 
participants of the focus group discussions, referring to the flexible nature of the suggested modular approach. 
Some concerns were raised on the proposed adjustment methods and how the study team would address the 
rising inflation and revise the rates accordingly. 

The third focus group discussion was used to present and discuss the preliminary study results on Areas 2 
and 3. The stakeholder consultation was crucial in verifying whether the preliminary study findings were clear 
and  relevant  for  the  participating  Member  States.  It  also  helped  the  study  team  to  anticipate  and  flag  any 
possible issues to be addressed in the later stages of the project. Finally, the preliminary discussions on the audit 
trail helped the study team to develop and propose audit trail requirements for the shortlisted Alternatives. 

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Other stakeholders within  the  European Commission, such as DG ENER, have also been consulted. During 
these consultations, DG ENER expressed concerns over the modular unit cost rates and whether they are based 
on CO2 reductions directly attributable to costs of a particular activity / module. In addition, some reservations 
were  expressed  about  the  sample  size  of  the  historical  data  collected  from  the  Member  States  and  the 
overreliance  on  statistical  methods  used  to  derive  SCO  rates.  Some  concerns  regarding  the  extrapolation 
method which predicts values for countries with partial / missing historical data were put forward. Here, the 
question was raised on how accurately the rates were calculated without considering the capacity of different 
Member  States  to  reduce  the  CO2  emissions,  as  the  initial  proposal  of  the  statistical  indicator  used  for 
extrapolations only considered price variation across the EU.  

The  second  presentation  to  members  of  the  Transnational  Network  of  ERDF/CF  SCO  practitioners  was 
meant to present the outcomes of SCO development in Areas 2 and 3 and the feasibility checks.  Participants 
were invited to discuss in groups and provide feedback on the proposed SCOs in the respective areas and ask 
for clarifications on the material presented before the meeting (via background notes) and during the meeting 
(via presentations). A compiled list of questions posed by Member States and the responses by the study team 
are presented in the table below (responses to questions received for feasibility checks are presented in section 
1.2). 

AREA 
All areas 

QUESTION 
Can we use EU-level SCOs at a lower level when we 
already know that we are not able to cover all the 
necessary cost of the activities? 

RESPONSE 
SCOs proposed in this study are meant to be used at EU-level. 
However, they are based on historical data about similar 
operations implemented in the past and, therefore, should be 
suitable for using at lower levels as well. 

All areas 

Which parameters have been taken into account to 
extrapolate the figures for those MSs which did not 
provide data? 

All areas 

All areas 

All areas 

All areas 

All areas 

The audit trail mentioned related to the EU-level 
SCO is a recommendation as a minimum 
requirement or it has to be further extended by the 
auditors if necessary? 

Is it compulsory to use an EU-level SCO only for 
some operations of the OP and not for all operations 
of the same type? 
In the case of a similar programme-specific SCO 
already developed and being used on a programme 
level, is it possible to switch for the EU specific once 
the latter has been published and proven more 
beneficial for beneficiaries? 
Could Member States use the result of the study as a 
base to further develop or adapt it to other 
programme areas or sub-areas not covered to 
develop their own SCOs? Can the data collected be 
used for this purpose? 
Can a correction coefficient be developed / 
introduced to correct big differences within a 
country and also on the different cost of the mission 
destination (for the internationalisation, e.g. costs of 
a participation fair in Seoul is different to a mission 
to Turkmenistan, for instance). 

For each operation type analysed in the study, the study team 
considered their cost composition to identify and define their 
main cost items. A selection of hand-picked statistical 
indicators best suited for explaining and predicting the cost 
variation of these cost items was then used for extrapolation, 
drawing in most cases on the labour cost index and price level 
index (for equipment, materials etc.) data. 
Audit trails for all proposed EU-level SCOs encompass all the 
necessary checks needed to prove that activities have taken 
place and that results have been achieved. Any further 
requirements for audit checks are at the discretion of the 
European Commission.  

The proposed EU-level SCO is intended to be used for all 
operations of the same kind supported by the OP. 

SCOs proposed in this study are meant to be used at EU-level. 
However, they are based on historical data about similar 
operations implemented in the past and, therefore, should be 
suitable for use at lower levels as well. 

Data collected throughout this study could be used by the 
Member States for future development of their own SCOs.  

Based on the available data, country coefficients at regional 
NUTS2 level would only be available for GDP PPP which is 
not a good proxy to calculate price level differences within 
different regions in a Member State. 

As regards differing costs for the mission destination, the 
study team has not found evidence to suggest that 
differentiation between different destinations is warranted. 

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AREA 
All areas 

Area 2 

Area 2 

Area 2 

Area 2 

Area 2 

Area 3 

QUESTION 
Adjustment methods: Is indexation an EU-level 
wide method or is it country specific. If a main 
framework change happens in a country that it is 
not reflected in the indexation method proposed, 
how can the SCOs be adjusted (can individual 
countries develop a different indexation 
methodology)?  
Sub-area 2A. How to identify what is the new 
position that did not exist before in the SME? 

Sub-area 2A. The EU-level SCO proposed in this 
sub-area is a unit cost for one month of gross new 
working position (that did not exist before) to 
directly perform R&D activities in the SME. This 
indicator penalises those SMEs who use their own 
resources to invest in this area. A similar output 
indicator will ensure flexibility in this respect and 
will not change the calculation of this unit cost. Is it 
possible to change this to an output indicator rather 
than a result-based one? 
Sub Area 2A. Could maternity leave be included in 
the methodology and considered as eligible 
expenditure? 

Sub area 2A. It was mentioned in the presentation 
that ‘re-using already existing unit cost rates from 
other union policies was seen as not favourable.’  
Please clarify why this is not favourable? 
Sub-area 2B. ‘There is a risk of double-funding when 
using the proposed EU-level SCOs. The beneficiary 
should ensure that all costs claimed are eligible.’  
Does this also involve further examination and 
verification of documents for the eligibility of 
expenses from the IB/MA?  
If yes, how does this contribute towards 
simplification? The IB/MA should only ensure the 
outputs / results not actual costs incurred. 
Sub-area 3A. How can the lump sum for networking 
activities of SMEs be applied at a lower level for MA 
and beneficiaries? 

Area 3 

Sub-area 3A. International events – is a common EU 
SCO planned? 

RESPONSE 
The adjustment methods for EU-level SCOs are common for all 
Member States. They are based on statistical indicators from 
Eurostat. Indexation methodologies individually tailored to 
each Member State are not applicable to EU-level SCOs.  

The definition of a new position that did not exist before 
closely follows the definition provided under the ERDF/CF 
common indicator methodology, as indicated in the summary 
table of the proposed SCO for sub-area 2A.  
This is essentially a 'before-after' indicator which captures the 
part of the employment increase that is a direct consequence of 
project implementation or its completion. 
The  purpose  of  this  SCO 
intersectoral, 
international mobility between research institutions and SMEs 
for  the  purpose  of  improving  the  research  capacity  of  the 
respective SME as they employ additional research staff. This is 
to be done through ERDF/CF financing.  
It  is  unclear  for  the  study  team  on  how  the  change  of  the 
indicator  from  a  result  to  output-based  would  overcome  the 
obstacle indicated in the question. 

is  to  facilitate 

This is not feasible at this stage of the study, since the study 
team does not have the data required for such analysis. 

The study team, after further discussions with DG REGIO, has 
elected to prioritise data collected in the study as the basis for 
developing EU-level SCOs. 

It is not necessary for the IB/MA to further examine and verify 
documents for the eligibility of expenses. The IB/MA will only 
ensure that outputs / results achieved are verifiable and real 
and that activities have taken place which are relevant for the 
operation. 

The implementation mechanism of the SCO is the same as 
applied at a higher level between the EC and the MA. Namely, 
to trigger reimbursement the beneficiary has to present proofs 
that eligible activities have taken place (i.e. documents 
providing evidence of actual attendance at the international 
event). It is worth noting that while these SCOs are formally 
designed for the upper level, they are specifically made to be 
applied at lower levels. 
It is unclear what the question exactly means. If the expression 
'common EU SCO planned' refers to a single SCO rate that 
applies to all the Member States, then the answer is no. 
Different rates were calculated for each Member State, 
specifically for 6 Member States using historical data. For the 
remaining 21 Member States, rates were derived through 
statistical inference. 
The lump sums calculated for sub-area 3A, which refer to SME 
trade fair participation costs, cover all the eligible costs 
specifically related to the activity in question. 

Area 3 

Area 3 

Sub-area 3A. Is it possible to declare other eligible 
expenditure together with SME participation at 
trade fairs? 

Sub-area 3B. Must the international strategy be 
outsourced or is it possible to hire somebody for this 
job?  

The development of an international strategy can be 
approached in both ways: outsourcing or hiring someone for 
the job. There is flexibility in choosing the method that best 

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AREA 

QUESTION 

Source: prepared by study team. 

RESPONSE 
suits the SME's needs and resources. However, regardless of 
the approach chosen, it is crucial to ensure that the 
internationalisation strategy holds value and meets the 
necessary requirements for reimbursement. For this purpose, 
the selection procedure should aim to award projects that 
demonstrate a clear added value in terms of 
internationalisation. 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

1.2.  EU-level result-based tools 

Simplification measures under EU cohesion policy 2021-2027 include a ‘Financing Not Linked to Costs’ 
option. This is a continuation of the ‘payments based on conditions’ option introduced in the Omnibus 
Regulation21. FNLC is based on fulfilling conditions related to progress being made in implementing  
programmes  or  the  achievement  of  their  objectives.  It  represents  a  radical  simplification  in 
implementation  as  it  changes  the  focus  from  costs,  reimbursement  and  checks  linked  to  individual 
projects to tracking deliverables and results for a project, group of projects or scheme.  Audits of the 
FNLC aim exclusively to verify that the conditions for reimbursement have been fulfilled. 

As part of the study to develop EU-level Simplified Cost Options and other EU-level results-based tools 
in the programming period 2021-2027 (ERDF/CF SCOs 1), the study team was expected to conduct up 
to three feasibility checks for the development of EU-level Financing Not Linked to Costs solutions, 
focusing on areas that would be potentially feasible and relevant for developing those tools. 

In accordance with Article 5(1) of Regulation (EU) 2021/1060, the ERDF/CF shall support, through its 
investments,  five  Policy  Objectives  (POs):  1)  a  more  competitive  and  smarter  Europe;  2)  a  greener, 
carbon-free and resilient Europe; 3) a more connected Europe; 4) a more social and inclusive Europe; 
and 5) a Europe closer to citizens.  

In our experience with feasibility checks, this exercise can take many different forms depending on the 
area covered by the analysis and the way the analysis object is defined. In the present document, the 
study team proposes a rather flexible but comprehensive approach towards this exercise. This section 
elaborates on our approach, elaborating on the steps taken to define the selected investment area, as 
well as validating our main findings and suggestions. 

Preparatory desk research 

As the first step, our team conducted preparatory desk research in all areas considered for the FNLC 
development. The main goal of this preparatory step was to identify and familiarise ourselves with the 
sources important for reconstructing the intervention logic of actions / priorities / projects supported in 
the analysed area.  

To this end, the study team revised all Policy Objectives for the ERDF/CF funds in the programming 
period 2021-2027 according to Article 5(1) of Regulation (EU) 2021/1060, namely PO1 (Smarter Europe); 
PO2  (Greener  Europe);  PO3  (Connected  Europe);  PO4  (Social  Europe);  and  PO5  (Europe  closer  to 
citizens).  

After preliminary desk research, the team was able to identify the following five investment areas with 
potential for an EU-level FNLC solution: 

•  Skills  for  smart  specialisation  and  transition (RSO1.4  –  ‘Developing  skills  for  smart 

specialisation, industrial transition, and entrepreneurship’); 
•  Digital connectivity (RSO1.5 – ‘Enhancing digital connectivity’); 

21 Regulation of the European Parliament and of the Council on the financial rules applicable to the general budget of the Union, amending 
Regulations (EU) No 1296/2013, (EU) No 1301/2013, (EU) No 1303/2013, (EU) No 1304/2013, (EU) No 1309/2013, (EU) No 1316/2013, (EU) No 
223/2014, (EU) No 283/2014, and Decision No 541/2014/EU and repealing Regulation (EU, Euratom) No 966/2012 
(https://data.consilium.europa.eu/doc/document/PE-13-2018-INIT/en/pdf). 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

•  Climate change adaptation (RSO2.4  –‘Promoting climate  change adaptation and disaster 

risk prevention and resilience, taking into account ecosystem-based approaches’); 

•  Sustainable urban mobility (RSO2.8 – ‘Promoting sustainable multimodal urban mobility, 

as part of the transition to a net zero carbon economy’); 

•  Sustainable transport (RSO3.2 – ‘Developing and enhancing sustainable, climate resilient, 
intelligent and intermodal national, regional and local mobility, including improved access 
to TEN-T and cross-border mobility’). 

The  shortlisted  areas  were  selected  based  on  the  following  criteria:  1)  investment  areas  covered  by 
Policy Objectives that are considered as main priorities for the ERDF and the CF (i.e. PO1 and PO2 in 
the case of the ERDF and PO2 and PO3 in the case of the CF)22; 2) areas with extensive Member State 
coverage, meaning that there are projects being implemented in multiple countries; 3) areas with higher 
availability of results indicators and financial data; 4) areas where EU-level simplification mechanisms 
have not yet been implemented under ERDF/CF. 

Member States consultation 

After  pre-selecting  the  most  promising  investment  areas  for  developing  FNLC  solutions,  the  study 
team consulted the Member States about the areas where EU-level result-based tools may be used. More 
specifically,  we  consulted  the  members  of  the  Transnational  Network  (TN)  on  simplification.  The 
consultation was carried out to further narrow down the list of investment areas pre-selected for the 
feasibility checks. 

Member  States  were  requested  to  rank  the  pre-selected  areas  on  a  scale  of  1  (highest  priority)  to  5 
(lowest  priority).  The  final  ranking,  based  on  replies  from  20  Member  States  (of  which  18  provided 
information on their preferred order) up to 19 April 2023, is presented in Table 4. 

Table 4. The ranking order of pre-selected investment areas 

RANK 

AREA 

Highest priority 

Skills for smart specialisation and transition (RSO1.4) 

Medium priority 

Digital connectivity (RSO1.5) 

Climate change adaptation (RSO2.4) 

Sustainable urban mobility (RSO2.8) 

Lowest priority 

Sustainable transport (RSO3.2) 

Source: prepared by the study team based on Member State consultation. 
Note: Final ranking was estimated by taking the median value attributed to each area and ranking them from the smallest to the 
highest value. The highest priority corresponds to areas where the median was ranked 1 or 2. Medium priority corresponds to 
a median of 3. The lowest priority areas are those in which the median was either 4 or 5. 

22 According to the EU cohesion policy priorities, available at: https://ec.europa.eu/regional_policy/policy/how/priorities_en.. Last access: 18 April 
2023. 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

The outcomes of this prompt consultation were used for further discussions with the Commission and 
informed the final selection of investment areas for the feasibility analysis. The selected areas for the 
feasibility checks were the following:  

Skills for smart specialisation and transition (RSO1.4) 

• 
•  Climate change adaptation (RSO2.4) 
• 
Sustainable urban mobility (RSO2.8) 

Second round of desk research 

After the shortlisted areas had been selected, the study team continued with data collection activities 
by carrying out additional (more in-depth and better-focused) desk research. The intended purpose 
of these research activities was to (i) elaborate further on the result indicators identified in previous 
steps, (ii) explore in greater detail the potential sources of data for estimations of costs / establishment 
of amounts, and (iii) assess the adequacy and sufficiency of these sources for the practical development 
of FNLC solutions (feasibility assessment). 

Meetings with Transnational Network of ERDF/CF SCO practitioners 

To validate the findings and conclusions of all feasibility checks, our team used the opportunity offered 
by this meeting to present the draft ad hoc reports to the Transnational Network of ERDF/CF SCO 
practitioners. As part of the presentation on the feasibility checks delivered by our team during these 
meetings, we asked for immediate feedback and the potential uptake of feasible FNLC solutions if they 
are developed in practice. The questions posed by Member States and our responses to these questions 
are summarised in the table below.  

AREA 

QUESTION 

ANSWER 

All areas  

Will  the  study  also  provide  the 

The  scope  of  the  study  was  to  access  the  feasibility  of  developing a 

acceptable verification method for 

FNLC  solution  in  the  three  selected  sub-areas.  The  study  team 

each  FNLC  Milestone  and  Target 

concluded that, at first glance, in all three areas, developing a FNLC 

devised 

through  which 

the 

solution, is feasible. Further developments, however, are outside the 

achievement of the Milestones and 

scope of this study. If the solutions proposed in the FCs are proven to 

Targets will be verified in order to 

be  possible  and relevant in  practice,  setting  milestones /  targets  and 

have  disbursements 

to 

the 

developing  the  calculations  would  be  the  next  logical  steps.  Partial 

Member States 

achievement of the milestones can be considered. 

Does  the  feasibility  check  also 

comprise  establishing  steps  to  be 

taken 

/  milestones 

for  FNLC 

schemes? 

Will the milestones be set for these 

FNLC’s  to  trigger  the  payment? 

Will the payments be done in part 

if some milestones are reached but 

not the full results? 

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Is the Commission going to assess 

the  reality  of  costs  proposed  for 

achieving milestones / targets?  

Has there been a discussion to use 

Data from RRF have proven useful in our FCs. In the case of the third 

RRF  data  of  MSs  for  all  three 

feasibility  check,  for  instance,  the  study  team  has  included  as  an 

areas? 

example the case of a programme in Croatia under the RRF aimed to 

reduce  the  risks  of  disasters.  If  there  are  more  examples  of 

programmes  under RRF  using  similar indicators in  other  areas also, 

they could also be used as inspiration for elaborating a FNLC solution.  

Promoting 

Should 

the  FNLC  prioritise 

FNLC does not consider only partial cost coverage, it is a tool used to 

sustainable 

funding  only  the  additional  costs 

cover the entire operation. 

multimodal 

for  the  more  sustainable  option 

urban 

mobility 

which may be more expensive? 

What 

is 

the  definition 

of 

Within the mobility system, multimodal mobility can be understood 

multimodal  for  the  purpose  of  

as the use of combined forms of transport such as road in combination 

funds? 

with  rail,  inland  waterway  and  sea  transport.  For  example,  projects 

related  to  multimodality  could  be  related  to improving connectivity 

What  is  the  link  between  the 

between  different  modes  of  transport  and  adopting  homogeneous 

multimodal  and 

the  mobility 

ticket systems  

system? 

Any idea how the get the ‘number 

Collection of data to set the baseline is necessary. If the data  are not 

of new users’ on the national level 

available, implementation of the FNLC mechanism with a delay can 

if you haven´t collected the data so 

be facilitated. 

far and don´t have a baseline? 

Clarification  is  needed  on  the 

All  the  indicators  we  analyse  in  the  FCs  are  common  ERDF/CF 

measurement methods for some of 

indicators, which are already well-established and being collected by 

the  proposed 

indicators.  For 

several  MS  in  the  current  period.  For  instance,  in  the  case  of  the 

example, number of cyclists using 

number of cyclists, the indicator is defined as the users of dedicated 

the infrastructure. 

cycling infrastructure financed by supported projects, which includes 

users  of  cycling  facilities  separated  from  roads  or  other  parts  of  the 

The results (people using the road 

same road by structural means (such as barriers), cycling street, cycling 

or  bikeway  happens  after  the 

tunnels  etc.  The  measure  of  the  indicator  should  follow  closely  the 

project  has  ended)  and  how  to 

definition of the official ERDF/CF common indicator RCR64 (annual 

measure it? 

users of dedicated cycling infrastructure).  

In  our  view counting the  bicycles 

is  an  issue,  it  would  need  to  be  a 

statistical estimate 

How  can  new  users  of 

the 

The 

indicators  define 

‘modernisation’  of  public  transport  as 

modernised 

infrastructure 

be 

‘significant improvements in  terms  of  infrastructure, and  access and 

measured in practical terms? 

quality of service’. The measure of the indicators should follow closely 

the definition of the official common indicators RCR62/RCR63.  

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What if the results are not durable? 

Partial achievement of the milestones could be considered. In addition, 

For  example,  if  the  bike  lane  is 

it  is  important  that  the  selection  procedure  focus  on  awarding  only 

broken two years after completion, 

projects with clear added value. 

will  the  full  amount  need  to  be 

reimbursed or only the part of the 

final milestones? 

Skills 

for 

What differentiates an SCO linked 

The main difference is that SCOs are linked to the cost of the operation, 

smart 

to  results  from  a  FNLC  linked  to 

while FNLC is not. 

specialisation 

results  for  this  policy  area  when 

and 

both are using CVT survey data? 

transition 

Is  it  possible  to  come  up  with 

It  is  possible  to  refer  to  the  official  definition  of  these  concepts 

specific  definitions 

of  what 

proposed by the EC. See, for example, the definitions proposed by the 

constitutes  smart  specialisation, 

JRC. 

industrial 

transition, 

and 

entrepreneurship 

to 

avoid 

different 

interpretations  during 

implementation? 

What  happens  after 

training? 

We acknowledge that, theoretically, this indicator seems to primarily 

Seems more like output indicators 

monitor output. However, as provided by Regulation (EU) 2021/1058 

rather than results. 

and  per  definition  of  the  European  Commission,  it  is  classified  as  a 

result indicator. 

What  is  the  link  between  the 

Vocational  training  encompasses  skill  development  as  well  as 

employees and company? How to 

innovation diffusion, both of which can potentially contribute to the 

measure 

the  gain 

that 

the 

sustainable economic growth of SMEs. It is important to note that our 

company gets? 

measurement focuses solely on the gain in terms of new staff trained, 

rather than capturing the broader impact on the SMEs. 

Are you not afraid  that there will 

The study team has taken this risk into consideration. To address this 

be  a  lack  of  quality  if  we  focus 

issue  and  mitigate  its  consequences,  it  is  advisable  to  implement  a 

mainly 

on 

the 

‘countable’ 

quality  assessment  process.  One  way  to  do  this  is  by  requesting 

outcomes  without  defining  the 

detailed information on the topics covered in the training. This enables 

quality criterion? 

a general evaluation of the training's relevance and alignment with the 

intervention  field.  Furthermore,  during  project  selection,  emphasis 

should be placed on quality. In this way the focus can be maintained 

on both the 'countable' outcomes and the quality of the training. 

Your  example  on  the  verification 

Yes, indeed, this suggestion will be duly considered in the feasibility 

mechanism only mentioned proof 

check 

of 

course 

attendance 

but 

employment  would  also  need  to 

be  checked  if  the  indicator  is 

employee 

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What kind of adjustment methods 

Microeconomic indicators specifically targeting training services will 

will  be  used  (apart  from  general 

be identified for the adjustment method. 

macroeconomic indicators)? 

Can we use university students as 

The  operation  should  be  consistent  with  the  definition  of  the  ERDF 

‘employees’ or as ‘participants’ for 

common  result  indicator  RCR  98,  namely  'Number  of  participants 

example, 

if 

a 

scheme 

for 

from  SMEs  (including  micro  enterprises)  who  complete  training  /  

universities  is  developed  where 

activity for skills development for smart specialisation, for industrial 

students 

are  placed 

in 

an 

transition and  entrepreneurship'. The  indicator  focuses  on  training  / 

enterprise 

for 

a 

candidate 

activity  completion  by  employees  of  SMEs  therefore  university 

placement  period? 

students cannot be considered as 'employees' or 'participants' in this 

context. 

Climate 

change 

Have  you  considered  emission 

For the sub-area of climate change adaptation and risk prevention, the 

reductions  or  energy  efficiency 

study team did not consider these indicators as they were not being 

adaptation 

reductions  FNLC  as  a  possible 

measured by many MS in the current period, therefore considered less 

and 

risk 

feasibility?    

relevant  according  to  our  assessment  criteria.  In  addition,  all 

prevention 

programmes analysed in this sub-area were in some way related to the 

prevention or management of risks, which is the reason the FC focused 

on these types of interventions. We must emphasise that the indicators 

chosen were a suggestion when the study team explored the available 

data on current programmes, meaning that other indicators could still 

be considered if they are considered relevant to many MS. 

When 

it  comes 

to 

flooding, 

The  choice  of  indicators  was  based  on  two  criteria:  the  indicators 

flooding usually happens in areas 

should  be  result-based,  and  they  should  be  relevant  to  many  MS, 

which are sparsely populated. The 

assessed by looking at how many MS are using the same indicators in 

indicator chosen will not give you 

the current period. However, the study team acknowledges that there 

an adequate reimbursement if the 

are risks associated with the selected indicators. When performing an 

area 

affected 

is 

sparsely 

assessment of possible risks of the proposed indicator, we anticipated 

populated. Is it possible to tie such 

that beneficiaries might, for instance, choose to implement projects in 

measures to other indicators? 

more  populated  areas  to  receive  a  larger  outcome-based  payment, 

therefore  neglecting  less  populated  areas  that  could  potentially  be 

Some  indicators  might  not  be 

more prone to disasters. For this reason,  selecting projects with clear 

suitable 

for  specific  areas  of 

added  value  is  necessary.  It  is  important  to  note  that  the  indicators 

support.  For  example,  annual 

proposed  are  a  suggestion  based  on  the  available  data  on  current 

users  of  new 

/  modernised 

programmes.  Other  indicators  could  still  be  considered  if  they  are 

transport  may  not  justify  support 

deemed as more relevant to many MS. 

to  less  populated or remote  users 

but  at  the  same  time  these  areas 

need most support. 

Why  you 

chose 

‘population 

affected’  as  an  indicator  and  not 

some other option, e.g. m2 of area? 

(especially for prevention of fires, 

but also applicable for others) 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

Gaps in data, how will you handle 

Gaps  in  data  can  either  be  handled  by  finding  complementary  data 

it?  

sources or by using statistical techniques to estimate the missing data, 

such as extrapolation. For instance, in the case of the disaster damage 

databases  that  was  mentioned  as  a  potential  source  for  sub-area 

‘climate  change  adaptation  and  risk  prevention’,  there  are  several 

different databases compiling information about disasters, that could 

potentially be used to offset the lack of data of the main data source. 

How  will  the  study  consider  the 

It  is  important  to  have  sufficient  data  points  to  consider  territorial 

territorial  differences  that  will 

differences, so that the averages are a better reflection of each country. 

define the type of remedial actions 

In the case of disasters datasets, for instance, each disaster is a different 

that  you  will  implement  as  a 

data point, which would allow the selection of more relevant events to 

response  to  the  environmental 

be included in calculations of these averages. 

risk? 

What  risk  assessments  have  been 

The risk assessment that should be performed for the release of funds 

taken 

into  account 

for 

risk 

should  follow  the  definition  and  measure  of  the  official  ERDF/CF 

prevention  /  compensation  costs 

indicators  RCR35/RCR36/RCR37/RCR96.  For  instance,  in  the  case  of 

e.g.  fire  or  floods?  Can  examples 

flood  protection  measures,  it  should  follow  the  definition  of  the 

be provided? 

indicator  RCR35,  an indicator  that  counts the resident  population at 

risk  of  flooding,  defined  as  the  population  living  in  areas  where 

protection  infrastructure  (including  also  green  infrastructure  for 

adaptation  to  climate  change)  is  built  or  significantly  upgraded  in 

order to reduce vulnerability to flood risks. 

Source: prepared by study team. 

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2.  Study findings 

2.1.  Area 1: SCOs for Energy Efficiency & Renewable 

Energy 

This section of the report provides the results of proposed SCOs in the policy area of Energy Efficiency 
and Renewable Energy. A key policy objective of Cohesion Policy in the new programming period is a 
greener, low-carbon Europe by promoting clean and fair energy transition, green and blue investment, 
the circular economy, climate adaptation and risk prevention and management. This policy objective 
will  be  supported  from  ERDF  and  CF  among  other  measures  through  promoting  energy  efficiency 
measures and renewable energy. The development of EU-level SCOs in the area of Energy Efficiency 
and Renewable Energy is divided into three sub-areas: 

•  Energy Efficiency & Renewable Energy in SMEs (sub-area 1A); 
•  Energy Efficiency & Renewable Energy in Buildings (public and housing sectors) (sub-

area 1B); 

Based  on  the  availability  of  data,  two  alternatives  of  a  unit  cost  were  considered  for  each  sub-area 
under Area 1: 

-  Alternative  A:  a  set  of  unit  costs  linked  to  common  ERDF/CF  indicators  CO30  (for  energy 
efficiency in SMEs), CO31 (for energy efficiency in residential buildings), and CO32 (for energy 
efficiency in public buildings) 

-  Alternative B: a unit cost linked to common ERDF/CF indicator CO34 (for energy efficiency in 

all three sectors) 

The rationale behind these alternatives was to build on a common / widely used and, preferably, result-
based indicator. This simplifies the task for Member States when it comes to measuring and reporting 
their achieved results in the future. The definitions of proposed SCO alternatives are presented in Table 
5 below. 

Table 5. Alternatives considered for further SCO development under Area 1 

Energy efficiency and 

renewable energy in SMEs 

(sub-area 1A) 

Energy efficiency and 

renewable energy in 
residential buildings (Sub 

Area 1B.1) 

Energy efficiency and renewable energy 

in public buildings (Sub-area 1B.2) 

Alternative A: Cost of one 
MW of additional capacity of 

Alternative A: Cost of 
improving energy 

Alternative A: Cost of decreasing the 
annual primary energy consumption by 

renewable energy produced 

consumption classification per 

1 kWh (CO32) 

(CO30) 

one household (CO31) 

This result-based SCO would reflect the 

This result-based SCO would 
reflect the average cost of one 

This output-based SCO would 
reflect the average cost of 

average cost of decreasing the annual 
primary energy consumption by 1 kWh. 

MW of additional capacity of 

improving energy consumption 

Values should be calculated based on 

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Energy efficiency and 
renewable energy in SMEs 

(sub-area 1A) 

Energy efficiency and 
renewable energy in 

Energy efficiency and renewable energy 

residential buildings (Sub 

in public buildings (Sub-area 1B.2) 

Area 1B.1) 

renewable energy produced. 
The additional capacity of 

classification per one 
household. The improvement 

information from the energy certificates 
issued before and after the 

renewable energy must be the 
direct consequence of the 

of energy consumption class 
must be the direct consequence 

reconstruction. The indicator will show 
the total decrease of annual consumption, 

project completion. 

of the project completion. 

not the total saved consumption. The 
decrease in energy consumption must be 

the direct consequence of the project 

completion.  

Alternative B: Cost of decreasing the annual GHG emissions by one ton of CO2 (CO34) 

This result-based SCO would reflect the average cost of decreasing the annual GHG emissions by one ton of 

CO2. The decrease of annual GHG emissions must be the direct consequence of the project completion. 

Source: prepared by the study team. 

After assessing the historical data sample, we chose to move forward with Alternative B (CO34). The 
choice to move ahead with indicator CO34 for both sub-areas was primarily a data-driven decision: 

- 

- 

- 

In sub-area 1A (SMEs), CO34 was the most common indicator according to historical data 
provided to the study team. The main limitation of its closest alternative is that CO30 
measured the increase of renewable energy production capacity and did not consider the 
energy saving potential as a direct consequence. 
In sub-area 1B.1 (residential buildings), data availability for indicators CO31 and CO34 was 
higher than for CO32 and CO30. The main caveat of CO31 is that it was used to count the 
number of households with improved energy class, not to measure the actual energy savings. 
Accordingly, in the data provided to the study team on CO31, only count of outputs (i.e. 
number of households with improved energy consumption classification) and average 
achieved energy class of renovated buildings was evident. 
In sub-area 1B.2 (public buildings), availability of data for CO32 and CO34 was similar, and 
in both cases higher than for other indicators (CO31 and CO30). However, CO32 is only 
applicable to public buildings and cannot be extended to other sub-areas. In this regard, it 
had lower simplification potential compared to SCO based on CO34. 

Based on the data quality assessment and the granularity of cost data collected, the study team proposes 
two approaches for the calculation and establishment of SCO values in the two sub-areas of Area 1. The 
key difference of these two approaches is that: 

•  Approach 1 first establishes rates at operation level implemented by a Member State 

which is then used as the basis for further extrapolations; 

•  Approach 2 first establishes proxy rates at activity level which are then used as the basis 
for further extrapolations. The sum of the proxy rates then corresponds to the rate set at 
operation level. 

Approach 1 

1.  Choice of the numerator (costs). The numerator is the total verified cost of the operation 

that facilitates a standard set of activities. 

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2.  Definition of the numerator (costs). For each Member State that provided data for a 

standard set of supported activities, the amount used as the numerator refers to costs of all 
types of activities funded and eligible under interventions implemented in that country. 
3.  Definition of the denominator (result). The amount used as the denominator refers to the 
common ERDF/CF indicator, which (according to the historical data provided to the study 
team) is the most widely used across Member States. 

4.  Extrapolation measures. Extrapolation process is facilitated at operation level by using 

base data from Member States with all relevant activities which is then used to predict the 
rates at operation level for Member States without sufficient historical data. 

In this approach, operations historically implemented by Member States are covered in their entirety. 
Their scope is consistent across Member States in the historical sample, as they cover a standard set of 
activities. Extrapolations are feasible as the base data at intervention level are a close match and highly 
comparable in terms of their implemented operations, albeit for a small sample size. This approach is 
specific to sub-area 1A. 

Approach 2 

1.  Choice of the numerator (costs). The numerator is the sum of verified costs of each 

implemented activity within the operation. 

2.  Definition of the numerator (costs). For each Member State that provided at least partial 
historical data on supported activities (i.e. cost data broken down by activity), amount 
used as the numerator refers to costs of the predefined set of activities adjusted to make it 
comparable across all Member States irrespective of how distinct their interventions had 
been historically.  

3.  Definition of the denominator (result). The amount used as the denominator refers to the 
common ERDF/CF indicator, which (according to the historical data provided to the study 
team) is the most widely used across Member States.  

4.  Extrapolation measures. The extrapolation process is facilitated at activity level by using 

proxy rates from Member States with data on a full or partial set of activities which is then 
used to predict the proxy rates for Member States without sufficient historical data. As the 
last step, the proxy rates are summed to derive rates at operation level. 

In this approach, operations historically implemented by Member States are covered only to the extent 
they are comparable across all countries providing the historical data. To this end, statistical inference 
(interpolation) was used to establish the cost and expected outcomes for a standardised list of activities 
in each Member State providing at least partial historical cost data at activity level. This approach is 
specific  to  sub-area  1B  to  accommodate  a  larger  base  data  sample  and  take  advantage  of  the  wider 
availability  of  activity-level  data  on  incurred  costs  (i.e.  both  full  and  partial  set  of  activities  are 
considered). 

2.1.1.  Sub-area 1A: Energy Efficiency & Renewable Energy in SMEs 

Definition of the SCO 

This result-based SCO would reflect the average cost of decreasing the annual GHG emissions by one 
ton of CO2. This SCO covers the cost of interventions aimed directly at reducing energy consumption 
and increasing the production of renewable energy by implementing energy saving measures in SMEs. 
The decrease of annual GHG emissions must be the direct consequence of the project completion and 
facilitate eligible activities. 

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Assessment of data 

The historical (base) data were collected directly from Managing Authorities and Intermediate Bodies 
by utilising a standardised and structured data collection form. The data collected from Member States 
rely on verified expenditure that comes directly from ERDF/CF. 

In the case of Area 1A, from 2 Member States (IT, PL) were used to calculate the SCO rate referring to 
a set of eight activities:  

energy audits to identify, quantify and report existing energy consumption profiles, 
replacement of window frames / glass / fixed shading systems in SMEs, 

• 
• 
•  upgrade of thermal insulation in SMEs, 
•  upgrade of heating / cooling systems (including based on RES) in SMEs, 
• 
• 
• 
• 

installation of hot water system with the use of RES in SMEs, 
installation of photovoltaic system in SMEs, 
smart management systems in SMEs, 
consultancy service for preparing the necessary technical studies / reports as well as 
monitoring the implementation of the interventions in SMEs. 

For  the  remaining  25  Member  States  that  provided  data  just  for  some  (BG  and  FR)  or  none  of  the 
activities,  the  total  cost  of  the  intervention  was  extrapolated  on  the  available  sample  (IT  and  PL). 
Differently than for area 1B, for this area the extrapolation was made at the level of the entire operation 
(i.e. based on the total costs of the  operations) and not at activity level. This difference is due to  the 
more limited size of the sample of historical data collected. 

Calculation method 

The calculation process was facilitated with the following key steps: 

Step  1  –  calculation  of  proxy  rates  at  activity  level  for  the  Member  States  which  provided  all  the 
necessary data to calculate this SCO. In the case of sub-area A, it was possible to calculate proxy rates 
for each eligible activity (see listed below) for two Member States, namely IT and PL. For the concerned 
Member States these proxy rates were calculated as the average ratio of total verified costs incurred to 
implement the activity in question (e.g. upgrade of thermal insulation in SMEs) and the total estimated 
annual decrease of CO2 emissions achieved in reported ERDF/CF projects23. The following activities are 
covered by these rates: 

energy audits to identify, quantify and report existing energy consumption profiles, 
replacement of window frames / glass / fixed shading systems in SMEs, 

- 
- 
-  upgrade of thermal insulation in SMEs, 
-  upgrade of heating / cooling systems (including based on RES) in SMEs, 
- 
- 
- 
- 

installation of hot water system with the use of RES in SMEs, 
installation of photovoltaic system in SMEs, 
smart management systems in SMEs, 
consultancy service for preparing the necessary technical studies / reports as well as 
monitoring the implementation of the interventions in SMEs. 

23 Any outlier values among established proxy rates were excluded consistently with the approach presented in Box 2. 

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Step 2 – adjustment of established proxy rates to account for inflation. Since proxy rates were drawn 
from historical data on interventions implemented in the period 2014-2020, estimates obtained in Step 
1 had to be updated to align them with current (2022) prices. To this end, proxy rates were indexed 
according to average cumulative inflation of construction costs (for more details on the ‘construction 
cost index’ used see section 1.1.5) in the period 2014-2022.  

Step  3  –  statistical  inference  (extrapolation)  of  missing  proxy  rates.  For  the  remaining  25  Member 
States, rates for the entire cost of the interventions where calculated based on a linear regression. The 
regression draws on the following: 

- 

independent (predictor) variable – the extrapolation index combining PLI (i.e. index on 
machinery and equipment costs) and LCI (index on labour cost), 

-  dependent (response) variable – the established proxy rates at operation level (i.e. rates 

calculated in Step 1 and adjusted in Step 2). 

Step  4  –  adjustment  of  rates  to  EU  energy  efficiency  scoreboard24  (i.e.  EE  scoreboard,  overall, 
combined).  The  rates  extrapolated  in  Step  3  were  then  adjusted  according  to  the  level  of  energy 
efficiency of the different countries. More precisely, for each of the remaining 25 Member States: 

- 

- 

the difference between the EE scoreboard of the relevant Member State and the average EE 
scoreboard of Poland and Italy was calculated (△ EE scoreboard), 
the rate extrapolated in Step 3 was then multiplied by one minus the difference calculated 
in the previous point (i.e. 1 - △ EE scoreboard) as a lower EE scoreboard corresponds to a 
lower level of energy efficiency so higher cost of the interventions. 

It is important to highlight that the extrapolation method adopted for this sub-area differs from sub-
area 1B. For sub-area 1A it was not possible to run a multivariate regression model with two variables 
and get all the parameter estimates (e.g. degrees of freedom, r-squared, f-statistic, p- value etc.) due to 
the more limited size of the sample of historical data collected. 

Rates 

Table 6 below indicates the SCO rates proposed by the study team for sub-area 1A.  

Table 6. Proposed SCO rates in sub-area 1A 

MEMBER STATE 

SCO RATE, 
EUR/TON CO2 

MEMBER STATE 

SCO RATE, EUR/TON 
CO2 

AT 

BE 

BG 

CY 

CZ 

DE 

 8 131  

 9 461  

 1 368  

 4 109  

 3 478  

 6 238  

IE 

IT 

LT 

LU 

LV 

MT 

 4 292  

 5 775  

 1 581  

 7 695  

 1 790  

 5 092  

24 EU countries scoring tool for energy efficiency indicators and policies | ODYSSEE-MURE 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

MEMBER STATE 

SCO RATE, 
EUR/TON CO2 

MEMBER STATE 

SCO RATE, EUR/TON 
CO2 

DK 

EE 

ES 

FI 

FR 

GR 

HR 

HU 

Summary table 

 8 572  

 1 822  

 4 097  

7 639  

 5 668  

 2 755  

 1 877  

 1 764  

NL 

PL 

PT 

RO 

SE 

SI 

SK 

 8 345  

 1 605  

 3 297  

 1 142  

 8 434  

 3 520  

 3 072  

Definition of the 
SCO 

This SCO covers the cost of interventions aimed directly at reducing 
energy consumption and increasing the production of renewable energy 
by implementing energy saving measures in SMEs. 

Eligible activities 

•  Energy audits to identify, quantify and report existing energy 

consumption profiles, 

•  Replacement of window frames / glass / fixed shading 

systems in SMEs, 

•  Upgrade of thermal insulation in SMEs, 
•  Upgrade of heating / cooling systems (including based on 

RES) in SMEs, 
Installation of hot water system with the use of RES in SMEs, 
Installation of photovoltaic system in SMEs, 
Smart management systems in SMEs, 

• 
• 
• 
•  Consultancy service for preparing the necessary technical 

studies / reports as well as monitoring the implementation of 
the interventions in SMEs. 

Indicator name  

Tons of CO2 equivalent 

Measurement unit 
for the indicator  

Number of tons of CO2 equivalent, estimated as total annual decline at 
the end of the period. The estimate is based on the amount of primary 
energy saved and/or produced by the supported facilities in the given 
year (either one year following project completion or the calendar year 
after project completion). Saved energy should replace the production of 
non-renewable energy. Produced renewable energy should be GHG 
neutral and replace the production of non-renewable energy. The impact 
of non-renewable energy on greenhouse gases is estimated as total 
national GHG emission per unit of non-renewable energy produced.  

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Base calculation 
formula 

𝑺𝑪𝑶𝟏𝑨 =

𝑪𝒕𝒐𝒕𝒂𝒍

𝒕𝒕𝑪𝑶𝟐

 , where 

ttCO2 is the total decrease of the annual GHG emissions, in tons of CO2 

Ctotal is total costs of relevant activities, in EUR 

Eligible costs  

All eligible costs of the operation. 

Arrangements for 
the audit trail 

Proof that the results delivered by projects are verifiable and real: 

-  Energy Performance Certificates (EPCs) completed before and 
after the implementation of energy efficiency measures, or 
equivalent 

Key 
risks/limitations 
and measures to 
prevent perverse 
incentives  

Risk/limitation 1: ‘Locked’ 
interventions / path dependence, i.e. 
it is assumed that Member States 
historically implementing small-
/large-scale interventions will 
continue with implementation of 
small- / large-scale interventions. 

Mitigation measure: Member States 
are allowed to freely select the 
activities they intend to facilitate 
– they will be reimbursed 
through the same single SCO 
rate.  

Risk / limitation 2: SCO estimates are 
feasible only for Member States that 
provided the study with 
appropriate and sufficient historical 
data. 

Mitigation measure: statistical 
inference allows unit cost values  
to be established for all Member 
States based on cost and result 
indices.  

Risk / limitation 3: slicing of 
operation may result in 
overcompensation of the real costs 
incurred while implementing 
supported interventions. 

Mitigation measure: slicing would 
also result in lower primary 
energy savings / lower decrease 
in estimated energy consumption, 
i.e. poorer intervention results; 
furthermore, slicing possibility is 
important for the attractiveness 
and flexibility of SCOs. 

Method for regular 
adjustment of the 
rates  

SCO values can be adjusted regularly on the basis of the following 
indicator(s): 

-  Construction cost index (or producer prices), new residential 
buildings, Percentage change compared to same period in 
previous year 

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2.1.2.  Sub-area 1B: Energy Efficiency & Renewable Energy in Buildings 
(public and housing sectors) 

Definition of the SCO 

This result-based SCO would reflect the average cost of decreasing the annual GHG emissions by one 
ton of CO2. This SCO covers the cost of interventions aimed directly at reducing energy consumption 
and  increasing  the  production  of  renewable  energy  by  implementing  energy  saving  measures  in 
housing  and  the  non-residential  sectors.  The  decrease  of  annual  GHG  emissions  must  be  the  direct 
consequence of the project completion and facilitate eligible activities. 

Assessment of data 

The proposed SCO is based on historical data collected from the Member States on ERDF/CF calls and 
projects that supported relevant measures in this sub-area. As indicated at the beginning of the report, 
the base data sample for  this sub-area was expanded by merging the datasets for housing and non-
residential public buildings. This was done in order to better reflect the historical situation in Member 
States for two similar sectors and is also beneficial for extrapolation purposes as a larger base sample 
allows for more reliable and robust estimates for Member States without historical data.  

As a result of this merge, historical data needed for SCOs was available for 12 Member States, out of 
which there were 3 Member States with a full (IT, PL, GR) and 9 Member States with partial (BG, CY, 
CZ, DE, ES, FR, LU, MT, PT) set of historical data. These data also served as the baseline for calculating 
the rates for the remaining Member States. Activities considered in the analysis were the following: 

- 

- 
- 
- 
- 

consultancy service for preparing the necessary technical studies / reports as well as 
monitoring the implementation of the interventions (project management / supervision 
works), 
energy audits for buildings, 
installation of biomass energy system, 
installation of hot water system with the use of RES, 
installation of new high efficiency or upgrade of existing heating / cooling systems 
(including based on RES), 
installation of renewable electricity unit, 
replacement of window frames / glass / moving of fixed shading systems, 

- 
- 
-  upgrade of thermal insulation (walls, roofs, ceiling, etc.). 

The overall picture of the  base data used in the calculations of SCOs can be seen in  Table 7. For the 
remaining 15 Member States which did not provide historical data of sufficient quality, extrapolation 
measures were applied to derive proxy rates at activity level. 

Table 7. Assessment of historical data availability in sub-area 1B 

MEMBER 
STATE 

LEVEL OF 
GRANULARITY 

A1 

A2 

A3 

A4 

A5 

A6 

A7 

A8 

BG 

Project 

CY 

Call 

E 

E 

E 

E 

E 

E 

E 

H 

H 

H 

E 

E 

E 

E 

E 

E 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

MEMBER 
STATE 

LEVEL OF 
GRANULARITY 

A1 

A2 

A3 

A4 

A5 

A6 

A7 

CZ 

Project 

DE 

Project 

ES 

Project 

FR 

Project 

GR 

Call 

IT 

Project 

LU 

Call 

MT 

Project 

PL 

Call / project 

PT 

Project 

E 

H 

E 

E 

H 

H 

E 

E 

H 

H 

E 

E 

E 

H 

H 

H 

E 

E 

H 

H 

E 

E 

H 

E 

H 

H 

E 

E 

H 

E 

E 

E 

E 

E 

H 

H 

E 

E 

E 

E 

E 

E 

H 

H 

H 

H 

E 

E 

H 

E 

E 

E 

E 

E 

H 

H 

E 

H 

H 

H 

E 

E 

E 

H 

H 

H 

E 

E 

H 

H 

A8 

H 

E 

E 

H 

H 

H 

H 

E 

H 

H 

Note: the activities for which proxy rates based on historical data were calculated are marked with ‘H’. ‘E’ denotes gaps in the data that had 

to be filled with an extrapolated approximation of the analysed activities based on the available sample. 

Source: prepared by the study team. 

Calculation method 

The calculation process was facilitated with the following key steps: 

Step 1 – calculation of proxy rates at activity level for the Member States which provided the necessary 
data to calculate this SCO. In the case of sub-area B, it was possible to calculate proxy rates for each 
eligible activity for three Member States, namely GR, IT and PL. For the concerned Member States these 
proxy rates were calculated as the average ratio of total verified costs incurred to implement the activity 
in question (e.g. installation of renewable electricity unit or replacement of window frames /  glass / 
moving of fixed shading systems) and the total annual decrease of CO2 emissions achieved in reported 
ERDF/CF projects25. The following activities are covered by these rates: 

- 

- 
- 
- 
- 
- 

consultancy service for preparing the necessary technical studies / reports as well as 
monitoring the implementation of the interventions (project management /  supervision 
works), 
energy audits for buildings, 
installation of renewable electricity unit, 
installation of biomass energy system, 
installation of hot water system with the use of RES, 
installation of new high efficiency or upgrade of existing heating / cooling systems 
(including based on RES), 

25 Any outlier values among established proxy rates were excluded consistently with the approach presented in Section 1.4. 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

replacement of window frames / glass / moving of fixed shading systems, 

- 
-  upgrade of thermal insulation (walls, roofs, ceiling, etc.). 

For nine other Member States (BG, CY, CZ, DE, ES, FR, LU, MT, PT), proxy rates were established for 
an incomplete set of activities. The extent of these calculations was subject to historical data availability 
presented in Table 7. For example, in the case of BG, proxy rates were established only for activity 5 – 
installation of new high efficiency or upgrade of existing heating / cooling systems. 

Step 2 – adjustment of established proxy rates to account for inflation. Since proxy rates were drawn 
from historical data on interventions implemented in the period 2014-2020, estimates obtained in Step 
1 had to be updated to align them with current (2022) prices. To this end, proxy rates were indexed 
according to average cumulative inflation of construction costs (for more details on the ‘construction 
cost index’ used see section 1.1.5) in the period 2014-2022. 

Step  3  –  statistical  inference  (extrapolation)  of  missing  proxy  rates.  For  the  Member  States  that 
provided incomplete historical data (i.e. BG, CY, CZ, DE, ES, FR, LU, MT, PT) or were unable to supply 
the  study  with  any  relevant  historical  data  (all  remaining  EU  Member  States),  proxy  rates  were 
established using linear regression model. The regression draws on the following:  

- 

independent (predictor) variables: 

o 

o 

‘cost index’ combining PLI (i.e. index on machinery and equipment costs) and LCI 
(index on labour cost); 
‘result  index’  based  on  the  overall  energy  efficiency  scores  (combined  index  for 
households)  assigned  to  each  EU  Member  State  taken  from  the  EU  Energy 
Efficiency Scoreboard26. 

-  dependent (response) variable – the established proxy rates (i.e. rates calculated in Step 1 

and adjusted for inflation in Step 2). 

This multivariate extrapolation model works in a similar way to a simple bivariate linear regression. 
The key difference is that we examine the relationship between several different variables as we seek 
to determine how separate cost and result indices affect the extrapolated value. 

Step 4 – summation of proxy rates established in Step 1 (GR, IT, PL) or in Steps 1 & 3 (all other EU 
Member States) to derive a single unit cost rate per Member State. The proxy rates established for 
each  Member  State  at  activity  level  are  summed  to  establish  a  single  rate  applicable  to  the  whole 
operation. 

Rates 

Table 8 below indicates the calculated SCO rates proposed by the study team for sub-area 1B.  

26 https://www.odyssee-mure.eu/data-tools/scoring-efficiency-countries.html  

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Table 8. Proposed SCO rates in sub-area 1B 

MEMBER STATE  SCO RATE, EUR/TON 

MEMBER STATE 

CO2 

14 391 

15 381 

5 439 

9 076 

9 731 

14 147 

17 662 

6 959 

8 950 

12 556 

14 292 

8 124 

7 173 

4 954 

 AT  

 BE  

 BG  

 CY  

 CZ  

 DE  

 DK  

 EE  

 ES  

 FI  

 FR  

 GR  

 HR  

 HU  

Summary table 

 IE  

 IT  

 LT  

 LU  

 LV  

 MT  

 NL  

 PL  

 PT  

 RO  

 SE  

 SI  

 SK  

SCO RATE, EUR/TON 
CO2 

13 374 

12 151 

5 958 

18 005 

7 177 

8 582 

14 978 

5 036 

10 410 

6 948 

14 181 

9 213 

7 499 

Definition of the 
SCO 

This SCO covers the cost of interventions aimed directly at reducing 
energy consumption and increasing the production of renewable energy 
by implementing energy saving measures in housing sector buildings. 

Eligible activities 

•  Consultancy service for preparing the necessary technical 

studies / reports as well as monitoring the implementation of 
the interventions (project management / supervision works), 

•  Energy audits for buildings, 
• 
• 
• 

Installation of biomass energy system, 
Installation of hot water system with the use of RES, 
Installation of new high efficiency or upgrade of existing 
heating / cooling systems (including based on RES), 
Installation of renewable electricity unit, 

• 
•  Replacement of window frames / glass / moving of fixed 

shading systems, 

•  Upgrade of thermal insulation (walls, roofs, ceiling, etc.). 

Indicator name  

Tons of CO2 equivalent 

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Measurement unit 
for the indicator  

Number of tons of CO2 equivalent, estimated as total annual decline at 
the end of the period. The estimate is based on the amount of primary 
energy saved and/or produced by the supported facilities in the given 
year (either one year following project completion or the calendar year 
after project completion). Saved energy should replace the production of 
non-renewable energy. Produced renewable energy should be GHG 
neutral and replace the production of non-renewable energy. The impact 
of non-renewable energy on greenhouse gases is estimated as total 
national GHG emission per unit of non-renewable energy produced.  

Base calculation 
formula 

𝑺𝑪𝑶𝟏𝑩 =

𝑪𝒕𝒐𝒕𝒂𝒍

𝒕𝒕𝑪𝑶𝟐

 , where 

ttCO2 is the total decrease of the annual GHG emissions, in tons of CO2 

Ctotal is total costs of relevant activities, in EUR 

Eligible costs  

All eligible costs of the operation. 

Arrangements for 
the audit trail 

Proof that the results delivered by projects are verifiable and real: 

-  Energy Performance Certificates (EPCs) completed before and 
after the implementation of energy efficiency measures, or 
equivalent 

Key 
risks/limitations and 
measures to prevent 
perverse incentives  

Risk / limitation 1: ‘Locked’ 
interventions / path dependence, i.e. 
it is assumed that Member States 
historically implementing small- / 
large-scale interventions will 
continue with implementation of 
small- / large-scale interventions.  

Mitigation measure: Member 
States are allowed to freely select 
the activities they intend to 
facilitate – they will be 
reimbursed through the same 
single SCO rate. 

Risk / limitation 2: SCO estimates are 
feasible only for Member States that 
provided the study with 
appropriate and sufficient historical 
data. 

Mitigation measure: statistical 
inference allows unit cost values 
for all Member States to be 
established based on cost and 
result indices.  

Furthermore, a concerted effort 
was done to increase the sample 
size in sub-area 1B by merging 
data from housing and non-
residential sectors.  

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Risk / limitation 3: slicing of 
operation may result in 
overcompensation of the real costs 
incurred while implementing 
supported interventions. 

Mitigation measure: slicing would 
also result in lower primary 
energy savings / lower decrease 
in estimated energy 
consumption, i.e. poorer 
intervention results; furthermore, 
slicing possibility is important for 
SCO’s attractiveness and 
flexibility. 

Method for regular 
adjustment of the 
rates  

SCO values can be adjusted regularly on the basis of the following 
indicator(s): 

-  Construction cost index (or producer prices), new residential 
buildings, Percentage change compared to same period in 
previous year 

2.2.  Area 2: SCOs for Research & Innovation activities 

Solutions for SCOs in the area of Research and Innovation are key for SMEs within EU Member States 
to become / remain competitive by increasing companies' productivity, accessing new, higher added 
value markets and ultimately leading to sustainable employment creation in a context of fierce global 
competition.  In  this  respect,  the  fulfilment  of  specific  objectives  set  by  ‘Smarter  Europe’  such  as 
innovative & smart economic transformation, enhancing research and innovation capacities, promoting 
the uptake of advanced technologies and developing skills for smart specialisation, industrial transition 
and  entrepreneurship  is  pivotal.  The  development  of  EU-level  SCOs  in  the  area  of  Research  and 
Innovation is divided into three sub-areas: 

•  Knowledge / technology transfer activities of SMEs (sub-area 2A); 
• 

Innovation vouchers for SMEs for Research & Development implementation (sub-area 
2B); 

•  Research Development & Innovation research projects (sub-area 2C).  

Following the assessment of the quality of data collected from Member States and alternative sources, 
the study team has examined potential SCO alternatives which would be feasible to develop in two of 
the three sub-areas of Area 2 – please see Table 9 for the alternatives which were considered. 

Table 9. Alternatives considered for further SCO development under Area 2 

Knowledge / Technology Transfer Activities of 

Innovation Vouchers for SMEs for R&D 

SMEs (sub-area 2A) 

Implementation (sub-area 2B) 

Alternative A: Cost per one person month of 
intersectoral mobility of an R&D researcher 

Alternative A: Cost of one innovation voucher 
issued to an SME for completing innovation / 

working in SMEs 

digitisation activities 

This SCO would reflect the average cost of one 

month in intersectoral mobility between research 

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Knowledge / Technology Transfer Activities of 

Innovation Vouchers for SMEs for R&D 

SMEs (sub-area 2A) 

Implementation (sub-area 2B) 

institutions and SMEs. It could be used for the 
reimbursement of all eligible entries based on a 

single monthly rate. The researcher should come 
from an accredited research institution. The 

measurement of the indicator would concern the 
total researcher months that partook in intersectoral 

mobility activities.  

This lump sum to cover the cost of one innovation 
for  completing 
to  an  SME 
voucher 
innovation / digitisation activities.  

issued 

should  obtain 

SMEs 
research 
organisations  and  other  service  providers  which  are 

services 

from 

accredited by the National Accreditation Authority in 
the  respective  Member  State.  Here,  the  research 

organisation / institution is the service provider while 
the  SME  is  the  beneficiary.  For  the  lump  sum,  all 

eligible costs or part of eligible costs of an operation are 
calculated  in  accordance  with  predefined  terms  of 

agreement on activities and / or outputs.  

Alternative B: Cost per one gross new working 

Alternative B: Cost to support the introduction of 

position (that did not exist before) to directly 

new to the market products (CO28) 

perform R&D activities in the SME 

This SCO would cover the cost of one gross new 
working position (that did not exist before) to 

directly perform R&D activities, in full-time 
equivalents. This SCO should be used to fund 

exchanges of research and innovation staff between 
academic and non-academic organisations such as 

SMEs. 

This SCO would cover the cost of introducing a ‘new 

to the market’ product by the SME.  

The  indicator  measures  if  an  enterprise  receives 

support  to  develop  a  'new  to  the  market'  product  in 
any of its markets. Includes process innovation as long 

as  the  process  contributes  to  the  development  of  the 
the  aim  of  actually 
product.  Projects  without 

developing  a  product  are  excluded.  If  an  enterprise 
introduces  several  products  or  receives  support  for 

several projects, it is still counted as one enterprise. In 
the case of cooperation projects, the indicator measures 

all participating enterprises. 

Alternative  C:  Number  of  enterprises  cooperating 

Alternative  C:  Cost  to  support  the  introduction  of 

with research institutions (CO26) 

new to the firm products (CO29) 

This  SCO  would  cover  the  cost  of  one  successful 

This SCO would cover the cost of introducing a ‘new 

cooperation between an SME and research institution.  

to the firm’ product by the SME.  

The 

indicator 

is  measured  as  the  number  of 

The indicator is measured if an enterprise is supported 

enterprises that cooperate with research institutions in 
R&D projects. At least one enterprise and one research 

to develop a 'new to the firm' product. Includes process 
innovation  as  long  as  the  process  contributes  to  the 

institution  participate  in  the  project.  One  or  more  of 
institution  or 
the  cooperating  parties  (research 

development of the product. Projects without the aim 
of  actually  developing  a  product  are  excluded.  If  an 

enterprise)  may  receive  the  support  but  it  must  be 
conditional to the cooperation. The cooperation may 

enterprise  introduces  several  products  or  receives 
support  for  several  projects,  it  is  still  counted  as  one 

be new or existing. The cooperation should last at least 

for the duration of the project.   

enterprise.  In  the  case  of  cooperation  projects,  the 
indicator  measures  all  participating  enterprises  to 

In  essence,  this  indicator  measures  the  networking 
activity  and  is  a  proxy  for  possible  technological 

transfer and knowledge exchange27. 

which the product is new. 

27 t33 srl, SWECO consortium, Development of a system of common indicators for European Regional Development Fund and Cohesion Fund 
interventions after 2020, p. 35 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

For  sub-area  2A,  the  study  team  considered  an  alternative  related  to  common  ERDF/CF  indicator 
‘Number  of  enterprises  cooperating  with  research  institutions’  (CO26)  which  was  one  of  the  more 
commonly mapped indicators from our data collection exercise. However, it is difficult to account for 
what  is  a  possible  successful  cooperation  between  SMEs  and  research  institutions  –  whether  it  is 
something that ends up in co-creation of goods or services, improvements in the business processes 
within the SME or other potential positive outcomes. Therefore, this Alternative was discarded fairly 
early in the development cycle. 

Alternative A in sub-area 2A was based on the Staff Exchanges action from Horizon Europe. An added 
value proposed by this Alternative is that it would facilitate synergies between the Directorate-General 
for Research and Innovation (DG RTD) and Horizon Europe towards ERDF/CF by opening up more 
opportunities  for  Cohesion  Widening  countries  that  are  relatively  disadvantaged  under  Horizon 
Europe. From the policy perspective, the Alternative was proposed in order to help fulfil objectives set 
out by ‘Smarter Europe’ such as increasing partnership between private  public research institutions 
and  SMEs.  However,  Alternative  A  relied  on  very  limited  data  (one  rate  which  is  applicable  to  all 
Member States). Additionally, re-using already existing unit cost rates from other union policies was 
seen as not favourable – instead, data collected during this study were prioritised which meant that 
Alternative B was selected for further analysis in sub-area 2A. 

For sub-area 2B, the study team also collected data for other indicators relevant to this sub-area, namely 
those related to the number of enterprises supported to introduce new to the market products (CO28) 
and number of enterprises supported to introduce new to the firm products (CO29). While in essence 
these  indicators  should  directly  reflect  one  of  the  key  purpose  of  innovation  vouchers,  it  must  be 
acknowledged that it is difficult to establish tangible typologies of products which would be eligible 
for  reimbursement.  For  instance,  defining  a  new-to-market  or  new-to-firm  product  requires  the 
development of typologies for each sector and almost make it a tier-list of products which are eligible 
for reimbursement. Additional checks would be necessary to assess whether the product is indeed new 
within the market or the firm, further complicating the prospective audit trail.  

In the case of sub-area 2C, while most Member States have provided historical data for this sub-area, 
the data collected were very heterogeneous in terms of supported activities, cost categories and outputs. 
As such, the data were not comparable across the Member States. An EU-level SCO merged with other 
sub-areas was considered. The study team explored the possibility of utilising the collected historical 
data by merging it with historical data from sub-areas 2A and 2B. Upon reviewing the available data, 
the study team discovered that merging data with other sub-areas was generally not feasible. The data 
provided in 2C covered a very large number of activities and outputs but did not have many overlaps 
with other sub-areas. It yielded limited results, such as additional project information for DK and LV 
in the case of sub-area 2B. However, merging such data on a larger scale would have increased the risk 
of inaccurate values since Member States implemented different activities and incurred different costs 
in these sub-areas.  

The calculation approaches are similar to those used in calculations of SCOs for the two sub-areas of 
Area 1: 

•  Approach 1 first establishes amounts at operation level implemented by a Member State 
which is then used as the basis for further extrapolations – this is applicable for the SCO 
developed for sub-area 2B; 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

•  Approach 2 first establishes amounts at cost item level which are then used as the basis for 
further extrapolations. The sum of the amounts at cost item level then correspond to the 
rate set at operation level. This is applicable for the SCO developed for sub-area 2A. 

2.2.1.  Sub-area 2A: Knowledge Technology Transfer Activities of SMEs 

Europe needs a strong and creative human resource base, mobile across countries and sectors, with the 
right skills to innovate and to convert knowledge and ideas into products and services for economic 
and social benefit. In this respect, enhancing research and innovation capacities is key to promoting a 
'Smarter Europe' – one of the key objectives for the European Commission in the context of Cohesion 
Policy. To fulfil this objective, ERDF/CF must focus on promoting knowledge and technology transfer 
activities aimed at encouraging cooperation and building connections between businesses and research 
entities including universities, research centres or knowledge-intensive companies in order to increase 
their innovation potential. 

One of the ways to facilitate knowledge and technology transfer is through temporary or permanent 
intersectoral mobility of researchers. Such interventions increase the absorptive capacity for R&D in 
firms,  strengthen  academia-industry  collaboration,  expand  intersectoral  networks  and  build  trust 
between  the  actors  involved.  In  the  context  of  projects  funded  by  the  ERDF  and  CF,  intersectoral 
mobility is often encouraged as a way to increase the impact of research and innovation activities. For 
example, researchers may be encouraged to work with industry partners to develop new products or 
services, or with government agencies to address specific societal challenges. 

Efforts to foster such intersectoral mobility are evident in most of the Member States, and they seem to 
have a positive impact on researchers’ skill development and employability as well as R&D intensity 
of  companies.  One  of  the  main  barriers  to  implementing  these  interventions,  however,  is  high 
administrative  burden  to  participating  firms28.  A  wider  application  of  EU-level  SCOs  in  concerned 
interventions supported by ERDF/CF could alleviate this barrier.  

To contribute towards this goal, the study team proposes: 

• 

a unit cost for the full-time equivalent per month of a gross new working position (that 
did not exist before) to directly perform R&D activities in the SME. 

Definition of the SCO 

The proposed unit cost covers the cost for the full-time equivalent per month of a gross new working 
position (that did not exist before) to directly perform R&D activities in the  SMEs. It is applicable to 
interventions  involving  exchanges  of  research  and  innovation  staff  between  academic  research 
performing organisations and SMEs. Both inter- and intra-state international mobility are covered by 
this SCO. 

The new research position must be a consequence of project implementation or completion, be filled 
(vacant  positions  are  not  counted)  and  increase  the  total  number  of  research  jobs  in  the  receiving 
organisation (i.e. the SME). Only employed personnel are counted. Support staff (i.e. jobs not directly 
involved in R&D activities) are not counted. Jobs can be full-time or part-time. Part-time jobs are to be 

28 Hristov, H., Slavcheva, M., Jonkers, K., Szkuta, K. (2016): Intersectoral mobility and knowledge transfer. Preliminary evidence of the impact of 
intersectoral mobility policy instruments. Available at: https://publications.jrc.ec.europa.eu/repository/handle/JRC102534 (Last accessed on 
28.05.2021) 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

converted  to  FTEs  per  month.  The  jobs  created  as  a  result  of  different  projects  should  be  added  up 
(provided that all projects receive ERDF/CF support); this is not regarded as multiple counting. Both 
new and already existing entities can be supported. 

Assessment of data 

The  proposed  unit  cost  is  primarily  based  on  historical  data  from  national  projects  and  calls  for 
projects  supported  by  ERDF/CF.  In  total,  eight  Member  States  (BE,  HR,  HU,  FR,  IT,  PL,  LU,  SI) 
provided  information  that  covers  all  data  points  required  for  calculations,  i.e.  verified  data  on 
project expenditure in the area of knowledge transfer activities, information on specific cost items29 
and the number of new research positions created as a result of implemented projects. A notable 
caveat of the historical data provided by PL and SI, is that their expenditure data were available 
only  at  a  higher  level  of  aggregation:  authorities  in  these  Member  States  provided  data  on  total 
verified costs of the operation without a breakdown per cost item. To mitigate this, missing activity-
level rates were interpolated by using the data on cost items from Member States which are available 
and relative shares of cost items eligible under intersectoral researcher mobility from MSCA Staff 
Exchanges. 

The  overall  picture  of  the  base  data  used  to  inform  calculations  can  be  seen  in  Table  10.  The 
remaining 18 Member States were unable to provide the requested historical data or their data were 
of  insufficient  quality.  Therefore,  proxy  rates  at  cost  item  level  had  to  be  extrapolated  for  these 
countries (see the next section for further details).  

Table 10. Assessment of historical data availability in sub-area 2A 

MEMBER 

STATE 

COST ITEMS 

CI1 

CI2 

CI3 

BE 

HR 

FR 

HU 

IT 

LU 

PL 

SI 

E 

E 

H 

H 

H 

H 

E 

E 

H 

E 

H 

E 

E 

H 

E 

E 

H 

E 

H 

E 

E 

E 

E 

E 

29 Data on the following cost items were used to establish the proposed rates: staff costs of the incoming researcher (cost item 1), research, 
networking and training costs (cost item 2), and management and indirect costs (cost item 3). 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

Note: the activities for which proxy rates based on historical data were calculated are marked with ‘H’. ‘E’ denotes gaps in the 
data that had to be filled with an extrapolated approximation of the analysed activities based on the available sample. 

Source: prepared by the study team. 

The development of this SCO also makes use of two complementary data sources: 

• 

Information from Marie Skłodowska-Curie Actions (MSCA): Staff exchanges action. 
The information collected from this alternative source was used to inform the 
extrapolation and interpolation exercise serving as proxy for standardised historical data 
missing  unavailable from the Member States. This source contains information on cost 
items related to intersectoral mobility of researchers, such as staff costs; research, 
networking, training costs and management costs  indirect costs. The relative share (in %) 
of these cost items in the total unit cost was used as a data point in both the extrapolation 
and interpolation30 processes. 

•  Data from the study ‘Monitoring data on ERDF and Cohesion Fund operations, and on 
the monitoring systems operated in the 2014-2020 period’. The study, among other 
things, collected data on total costs and results achieved in ERDF/CF projects, including 
projects in the area of research and innovation. It also collected relevant information on 
common ERDF indicators, such as CO24 (gross new working position (that did not exist 
before) to directly perform R&D activities in the SME). Here, 14 Member States (BE, CZ, 
DE, ES, FR, GR, HR, IE, IT, LV, PL, RO, SI, SK) have data on the total costs of the project 
and the number of implemented outputs for CO24 indicator).  This source was used as a 
triangulation source with the final SCO rates based on historical data collected during this 
study. 

Calculation method 

Step 1 – calculation of monthly rates in each project  call estimated at cost item level for the 8 Member 
States that provided historical data (BE, HR, HU, FR, IT, PL, LU, SI). 

Here, we use the total verified costs of a project per each cost item and the number of new gross research 
positions  (in  annual  FTEs)  created  as  a  consequence  of  project  implementation.  This  results  in 
calculations of annual rates for the gross working position in each project / call estimated at cost item 
level. Afterwards this figure is divided by 12 to arrive at a monthly rate for the gross working position 
in each project/call estimated at cost item level. This is facilitated for all 8 Member States in the historical 
sample (BE, HR, HU, FR, IT, PL, LU, SI). 

Step 2 – cleaning of available historical data provided by 8 Member States (BE, HR, HU, FR, IT, PL, 
LU, SI) Data for the following projects  were not included in further analysis based on the following 
qualitative criteria: 

-  Ongoing projects; 
-  Terminated projects; 
-  Projects involving state aid; 
-  Projects that did not include implemented outputs; 
-  Projects that include no expenditure; 
-  Projects for which the operation did not fit the analysed sub-area; 

30 Interpolation refers to statistical inference which is conducted within the sample. Extrapolation refers to statistical inference which is conducted 
outside the sample. 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

-  Projects involving Interreg programme. 

Step 3 – identification and exclusion of the outliers based on quantitative criteria for the 8 Member 
States (BE, HR, HU, FR, IT, PL, LU, SI) at cost item level. Outlier analysis is done in two sub-steps: 

- 

- 

First, the calculated rates are subject to the interquartile analysis (see section 1.1.3 for more 
details on this method); 
Then, the study team undertook an outlier analysis based on expert judgement in order to 
remove quantitative outliers within the historical sample of Member States in order to 
comply with the socio-economic logic. 

Step  4  –  calculation  of  average  monthly  rates  at  cost  items  level  for  the  8  Member  States  with 
historical data (BE, HR, HU, FR, IT, PL, LU, SI). Here, we calculate the average SCO of all project / call 
level rates prepared during step 1 which were retained data cleaning and outlier analysis presented in 
Steps 2 and 3. 

Step 5 – statistical inference (interpolation) of country average rates for missing cost items for the 8 
Member States with historical data (BE, HR, HU, FR, IT, PL, LU, SI). Here, the study team utilises the 
relative share (in %) of cost items under MSCA Staff Exchanges to fill in the gaps. Based on MSCA Staff 
Exchanges, the weights of the relative cost items are as follows: 

•  Cost item 1: Staff costs of the incoming researcher – 50 %  
•  Cost item 2: Research, networking and training costs – 28 % 
•  Cost item 3: Management and indirect costs – 22 % 

Step  6  –  adjustment  of  established  rates  to  account  for  inflation.  Since  rates  were  drawn  from 
historical data on interventions implemented in the period 2014-2020, estimates obtained in Steps 4 and 
5 had to be updated to align them with current (2023) prices. To this end, proxy rates at cost item level 
were indexed according to average cumulative inflation of labour costs for professional and research 
activities (for more details on this index, please see section 1.1.5) in the period 2014-2022. 

Step 7 – statistical inference (extrapolation) of rates or the remaining 19 Member States without any 
historical data. Rates for the entire cost of the operation were calculated based on a linear regression. 
The regression draws on the following: 

- 

independent (predictor) variable – the extrapolation index using LCI (index on labour 
costs for professional and research activities); 

-  dependent (response) variable – the established rates at operation level (i.e. rates 

calculated in Step 2). 

Step 8 – summation of proxy rates established in Steps 4, 5 and 6 (BE, HR, HU, FR, IT, PL, LU, SI) and 
in Step 7 (all other EU Member States) to derive a single unit cost rate per Member State. The proxy 
rates  established  for  each  Member  State  at  cost  item  level  are  summed  to  establish  a  single  rate 
applicable to the whole operation. 

Rates 

Table 11. Proposed SCO rates in sub-area 2A 

MEMBER STATE 

SCO RATE (€), MONTHLY FTE FOR NEW RESEARCH POSITION 

Austria 

7 045  

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MEMBER STATE 

SCO RATE (€), MONTHLY FTE FOR NEW RESEARCH POSITION 

Belgium 

Bulgaria 

Croatia 

Cyprus 

Czechia 

Denmark 

Estonia 

Finland 

France 

Germany 

Greece 

Hungary 

Ireland 

Italy 

Latvia 

Lithuania 

Luxembourg 

Malta 

Netherlands 

Poland 

Portugal 

Romania 

Slovakia 

Slovenia 

Spain 

Sweden 

6 581  

4 658  

4 395  

5 111  

5 469  

7 688  

5 312  

6 659  

7 179  

7 209  

6 036  

5 105  

6 938  

5 364  

5 063  

4 909  

9 956  

6 004  

7 459  

5 761  

5 702  

4 410  

5 982  

5 895  

6 009  

7 521  

Note: Green coloured values denote cost approximation based on historical data.  

Source: prepared by the study team. 

Summary table 

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Definition of 

the SCO 

Eligible 

activities 

This  SCO  would  cover  the  cost  of  one  gross  new  working  position  (that  did  not  exist 
before) to directly perform R&D activities, in full-time equivalents.  

All activities linked to intersectoral intra-state and intersectoral international mobility 
of between researchers and SMEs. This includes post-doctoral researcher placements 

in the SME and placements for industrial PhDs and traineeships at the SME. 

Indicator name  

Gross new working position (that did not exist before) to directly perform R&D activities 

Measurement 

Number  of  new research  positions,  in  full-time  equivalents.  The new  research  position 

unit for the 

indicator  

must  be  a  consequence  of  project  implementation  or  completion,  be  filled  (vacant 
positions are not counted) and increase the total number of research jobs in the receiving 

organisation (i.e. the SME). Only employed personnel are counted. Support staff (i.e. jobs 
not directly involved in R&D activities) are not counted. Jobs can be full-time or part-time. 

Part-time  jobs  are  to  be  converted  to  FTEs  per  month.  The  jobs  created  as  a  result  of 
different  projects  should  be  added  up  (provided  that  all  projects  receive  ERDF  /  CF 

support); this is not regarded as multiple counting. Both new and already existing entities 

can be supported. 

Base calculation 

𝑺𝑪𝑶𝟐𝑨 =

𝑪𝒕𝒐𝒕𝒂𝒍

𝒕𝒕𝑭𝑻𝑬𝒔

 , where 

formula 

ttFTEs  is  the  number  of  research  positions  created  as  a  result  of  intersectoral  mobility 

activities, in FTEs  

Ctotal is total costs of relevant operations, in EUR 

Eligible costs  

All eligible costs of the operation. 

Arrangements 
for the audit 

trail 

Proof of eligibility of researchers 

Academic  documentation  proving  the  eligibility  of  the  researchers.  The  following 
eligibility rules are set: 

•  Researchers  at  any  career  stage,  from  PhD  candidates  to  post-doctoral 
researchers, as well as administrative, technical or managerial staff involved 
in research and innovation activities; 

•  Researchers  must  be  engaged  in,  or  linked  to,  research  and  innovation 
activities  at  their  sending  organisation  for  at  least  one  month  prior  to  the 
secondment. 

Proof that eligible activities have taken place 

Work  contract  for  mobility  agreements  between  sending  organisation  (researcher)  and 

receiving organisation (SME). These agreements outline the terms and conditions of the 
mobility  arrangement  between  the  researcher  and  the  host  institution  or  organisation. 

They should include details such as the purpose of the mobility, the expected outcomes 
and the indicative length of the secondment. Both temporary or permanent work contracts 
are permitted under the SCO.  

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

Proof that the results delivered by projects are verifiable and real 

Enterprise  HR  documents  from  the  receiving  organisation  which  would  provide  the 
number of new FTEs created during the implementation of the project. 

Key 
risks/limitations 

Risk / limitation 1: There may be 
challenges in using a unit cost for 

The  proposed  EU-level  SCO  is  based  on  a 
the 
common  ERDF/CF 

indicator.  Hence, 

and measures to 
prevent 

perverse 

incentives  

researchers because of the way FTEs 

are calculated in SMEs. 

methodology for FTE counting should be known 
to the Member States. The final definition of the 

SCO  should 
the  ambiguity.  The 
description  states  that  full-time,  part-time  and 

reduce 

seasonal researchers should be accrued towards 
the total amount of FTEs created during project 

implementation. 

Risk / limitation 2: SCO estimates are 
feasible only to Member States that 

Statistical inference allows unit cost values  to 
be established for all Member States based on 

provided the study with appropriate 

and sufficient historical data. 

cost indices. Furthermore, resources gathered 
from similar operations within MSCA allows 

values within the historical sample to be 

extrapolated. 

Risk / limitation 3: Opportunities for 
intersectoral mobility of researchers 

Under Horizon Europe, those Member States 
with a less research-intensive environment find 

is already available under Horizon 

Europe (MSCA Staff Exchanges) 

it difficult to compete for research grants. The 
main added value of this alternative is that it 

facilitates synergies between RTD and Horizon 
towards ERDF/CF by opening up more 

opportunities for Cohesion / Widening 
countries that are relatively disadvantaged 

under Horizon Europe. 

Method for 
regular 

adjustment of 

the rates  

SCO values can be adjusted regularly on the basis of the following indicators: 

Labour cost index in professional, scientific and technical activities (Eurostat, 

LC_LCI_R2_A). Percentage change compared to previous year.  

2.2.2.  Sub-area 2B: Innovation Vouchers for SMEs for R&D 

Implementation 

Innovation  vouchers  are  small  grants  provided  to  SMEs  by  regional  or  national  governments  to 
purchase  services  from  knowledge  providers.  They  are  aimed  at  assisting  companies  to  invest  in 
innovative  solutions  and  services  or  to  acquire  machinery  that  will  facilitate  innovation.  Under  an 
innovation voucher programme, SMEs can apply for  a voucher that can be used to pay for  services 
provided by approved knowledge providers such as universities, research institutions, or other private 
sector organisations. The services may include research and development, technical assistance, or other 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

forms of specialised expertise that can help SMEs to improve their products, processes, or services. This 
type of voucher focuses broadly on innovation and not specifically on digitisation and environmental 
technology.  Additional  objectives  of  innovation  vouchers  include  promoting  science-industry 
collaboration,  stimulating  knowledge  transfer  and  fostering  the  formation  of  long-term  networks 
between  SMEs  and  public  and  private  research  partners.  Innovation  vouchers  should  be  demand 
driven  with  very  few  exceptions.  Cohesion  Policy  should  offer  SMEs  the  voucher  to  reimburse 
innovation activities more openly and should avoid locking in the operations to only fit specific types 
of innovation (such as creating start-up projects in environmental technology or digital products). In 
short, applying innovation vouchers to any kind of possible innovation context should be featured to 
ensure buy-in from a wider pool of potential beneficiaries. 

We propose the following EU-level SCO solution in this area: 

•  Lump sum to cover the cost of one innovation voucher issued to an SME for completing 

innovation / digitisation activities. 

Definition of the SCO  

This SCO would cover the cost of one innovation voucher issued to an SME for completing innovation 
/  digitisation  activities.  SMEs  should  obtain  services  from  research  organisations  and  other  service 
providers which are accredited by the National Accreditation Authority in the respective Member State. 

Here, the research organisation / institution is the service provider while the SME is the beneficiary. For 
the lump sum, all eligible costs or part of the eligible costs of an operation are calculated in accordance 
with predefined terms of agreement on activities and/or outputs. To claim reimbursement, Managing 
Authorities are required to provide proof as part of their audit trail that predefined activities have taken 
place. 

The following activities / cost items are to be covered by this SCO: 

•  provision of consultancy services provided to SMEs by universities, research centres or 

knowledge-intensive companies;  

•  development of the SME’s digital capabilities; 
• 
• 

costs of scientific and technical equipment and materials; 
costs of ICT instruments. 

It  is  important  to  note  that  the  historical  data  sample  collected  from  the  Member  States  includes 
information  on  vouchers  for  digitisation  and  innovation  vouchers  in  general.  The  thematic  areas 
covered under the vouchers are bottom up and therefore not only ‘environmental technology or digital 
products’.  Accordingly,  other  types  of  innovation  voucher  will  not  be  exempted  from  the  eligible 
operations by the proposed SCO. 

Assessment of data  

The proposed SCO is based on historical data collected from the Member States on ERDF/CF calls and 
projects that supported relevant measures in this sub-area. Overall, there are 11 Member States (Cyprus, 
Czechia,  Croatia,  Estonia,  France,  Hungary,  Italy,  Latvia,  Poland,  Portugal,  Malta)  which  have 
sufficient data to calculate this SCO.  

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The sample of projects for innovative start-up projects in environmental technology or digital products 
would  have  been  very  narrow,  therefore  the  sample  has  been  expanded  towards  creating  and 
improving  innovation  products  through  all  types  of  innovation  activities.  In  the  case  of  innovation 
vouchers,  even  though  the  whole  variety  of  relevant  ERDF/CF  have  been  analytically  untangled, 
categorised  and  structured  (into  types  of  services  and  by  the  type  of  costs),  there  are  underlying 
differences  as  to  how  Member  States  perceive  and  approach  actual  Research  and  Innovation  (R&I) 
activities that are covered under innovation vouchers. Sometimes a single service might mean more or 
less the same thing in different Member States (in terms of activities carried out), but the amount of 
resources  dedicated  to  its  provision  might  differ  significantly.  Taking  this  into  account,  an  outlier 
analysis has been conducted to arrive at a standard sample, both in terms of activities facilitated and 
costs  incurred  which  would  help  facilitate  the  extrapolation  process  for  Member  States  without 
sufficient  historical  data.  This  sample  consists  of  6  Member  States  where  a  sufficient  level  of 
disaggregation  and  accompanying  qualitative  information  was  available  (Czechia,  Croatia,  France, 
Italy, Poland, Portugal). 

Cyprus, Malta, Estonia, Hungary and Latvia have provided aggregated level data on a single call for 
innovation  vouchers.  As  a  result,  it  is  not  possible  to  account  for  outliers  in  these  cases.  For  the 
subsequent  extrapolation  process  for  Member  States  without  any  historical  data,  the  data  that  are 
within the base sample have to be both comparable in the type of operations and the costs incurred in 
order  to  make  the  extrapolation  model  work  based  on  the  assumption  of  socio-economic  logic. 
Furthermore, in most of these Member States, a minimum and maximum threshold for the prospective 
innovation voucher is set up, thus indicating that the aggregated call data contain a wide range of sizes 
of  the  innovation  voucher.  This  complicates  the  process  of  making  statistical  inferences  for  other 
Member States based on such calculations.  

Taking into account the assessment of available data in this area, the study team proposes two scenarios 

for arriving at lump sum rates for this sub-area. The difference of these scenarios is elaborated upon in 

the subsequent section. 

Calculation method 

Scenario 1: Lump sum rates established for 11 Member States based on the available historical data 

1.  Choice of the numerator (costs). The numerator is the total verified cost of the operation 

that facilitates activities specific to the analysed Member State. 

2.  Definition of the numerator (costs). For each Member State that provided data for their 

set of supported activities, the amount used as the numerator refers to costs of all types of 
activities funded and eligible under interventions implemented in that country. 

3.  Definition of the denominator (result). The amount used as the denominator refers to the 
most widely used, relevant and feasible indicator according to the historical data provided 
to the study team.  

In this scenario, operations historically implemented by Member States are covered in their entirety. 
Their scope is mostly consistent across Member States in the historical sample, as they cover the core 
activities  expected  to  be  facilitated  in  an  innovation  voucher  (for  example,  consultancy  services 
procured from research institutions). Extrapolations are not feasible as the base data at operation level 
are specific to the analysed Member State, for some of which the aggregated data at call level were not 
subjected to outlier analysis.    

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Scenario 2: Lump sum rates are established for 27 Member States based on the available historical 

data and extrapolations 

In this scenario, Steps 1-3 are the same as the previous scenario. However, extrapolation is used for 
Member States without historical data. The process is facilitated at operation level by using base data 
from Member States with all relevant activities which are then used to predict the rates at operation 
level for Member States without sufficient historical data. Extrapolations are feasible as the base data 
at operation level are standardised and all quantitative outliers have been removed due to sufficient 
granularity available in the historical data sample.   

The  two  scenarios  were  developed  with  consideration  given  to  the  ongoing  discussions  with  the 
European Commission and the apparent tentativeness to rely upon statistical methods to deduct EU-
level SCO rates. A detailed step-by-step approach of the calculation process based on both scenarios is 
provided in Table 12.  

Table 12. Calculation process for the development of SCOs for sub-area 2B 

SCENARIO 1 

SCENARIO 2 

Step 1. Calculation of the total SCO amount at project / call level 

The lump sum amount is calculated on the basis of the following data: total verified costs of a project and the 
number of innovation vouchers. This is facilitated for all 11 Member States of the initial historical sample (CY, 
CZ, EE, FR, IT, LV, PL, PT, HR, HU, MT) by using the following formula: 

𝑺𝑪𝑶𝟐𝑩 =

𝑪𝒕𝒐𝒕𝒂𝒍

𝒕𝒕𝒗𝒐𝒖𝒄𝒉𝒆𝒓𝒔

 , where 

ttvouchers is the number of innovation vouchers issued to SMEs, in units 

Ctotal is total costs of relevant operations in the project, in EUR 

Step 2. Cleaning of available historical data  

Data for the following projects are not included in further analysis based on the following qualitative criteria: 

-  Ongoing projects; 
- 
- 
- 
- 
- 

Terminated projects; 
Projects involving state aid; 
Projects that did not include implemented outputs; 
Projects that include verified expenditure; 
Projects involving an Interreg programme. 

Member States for which data do not comply with the 
standard  definition  (for  example,  Estonia  which 
in  addition 
funded  development  vouchers 
to 
for  which  data  are 
innovation  vouchers)  and 
insufficiently  granular  to  clean  are  removed  from 
further analysis. 

Step 3. Identification and exclusion of the outliers based on quantitative criteria 

Applicable to the 11 Member States (CY, CZ, EE, FR, 
IT, LV, PL, PT, HR, HU, MT) which provided all the 
necessary data to calculate the SCOs.  

Applicable to the 6 Member States (CZ, FR, IT, PL, PT, 
HR) in the historical data sample.  

- 

First, the calculated rates are subject to the 
interquartile analysis  

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SCENARIO 1 

SCENARIO 2 

The calculated rates are subject to the interquartile 

- 

analysis (see section 1.1.3 for more details on this 

method) 

Then,  the  study  team  undertook  an 
outlier 
expert 
analysis  based  on 
remove  quantitative 
judgement 
outliers  within  the  historical  sample  of 
Member States to comply with the socio-
economic logic. 

to 

Step 4. Calculation of the total SCO amount at operation level 

This  step  entails  calculating  the  total  SCO  for  the  11 
Member States which provided all the necessary data 
to calculate this SCO (CY, CZ, EE, FR, IT, LV, PL, PT, 
HR, HU, MT) at the operation level. Here, we calculate 
the average SCO of all project/call level rates prepared 
during step 1 which were retained in the data cleaning 
and outlier analysis presented in Steps 2 and 3. 

This  step  entails  calculating  the  total  SCO  for  the  11 
Member States which provided all the necessary data 
to calculate this SCO (CY, CZ, EE, FR, IT, LV, PL, PT, 
HR, HU, MT) at the operation level. Here, we calculate 
the average SCO of all project/call level rates prepared 
during step 1 which were retained in the data cleaning 
and outlier analysis presented in Steps 2 and 3. 

Step 5. Adjustment of established amounts to account for inflation 

Since rates were drawn from historical data on interventions implemented in the period 2014-2020, estimates 

obtained in Step 4 had to be updated to align them with current (2023) prices. To this end, proxy rates were 
indexed according to average cumulative inflation of labour costs for professional and research activities  and 

Harmonised Index of Consumer Prices in industrial goods, services and communication and (for more details 

on this index, please see Section Error! Reference source not found.) in the period 2014-2022.  

Step 6. Statistical inference (extrapolation) of lump sum rates for other Member States  

This  step  is  not  relevant  in the  case  of  Scenario 1, as 
extrapolation measures are not taken.  

Source: prepared by the study team. 

Rates 

For  the  remaining  21  Member  States  which  are  not 
covered by the historical data sample, lump sum rates 

were established using a linear regression model. The 

regression draws on the following:  

• 

independent (predictor) variables: 

o 

o 

‘labour cost index’ based on the 
LCI  (index  on  labour  cost  on 
professional services); 
‘equipment index’ based on the 
PLI (i.e. index on machinery and 
equipment costs)  

• 

dependent  (response)  variable  –  the 
established  proxy  rates  (i.e.  amounts 
calculated  in  Step  4  and  adjusted  for 
inflation in Step 5). 

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MEMBER 

STATE 

Austria 

Belgium 

Bulgaria 

Cyprus 

Croatia 

Czechia 

Denmark 

Estonia 

Finland 

France 

Germany 

Greece 

Hungary 

Ireland 

Italy 

Latvia 

Lithuania 

Luxembourg 

Malta 

Netherlands 

Poland 

Portugal 

Romania 

Slovakia 

Slovenia 

Spain 

Sweden 

SCENARIO 1. RATES BASED ON 
HISTORICAL DATA ONLY (€) 

SCENARIO 2. RATES BASED ON 
HISTORICAL + EXTRAPOLATIONS (€) 

N/A 

N/A  

N/A 

4 995 

9 455 

 8 053  

N/A 

18 649 

N/A 

13 061 

N/A 

N/A 

20 945 

N/A 

11 577 

14 707 

N/A 

N/A 

9 115 

N/A 

 8 136 

9 380 

N/A 

N/A 

N/A 

N/A 

N/A 

 12 689  

 14 038  

 6 908  

 7 921  

 9 455  

 8 053  

 14 207  

 8 477  

 11 690  

 13 061  

 13 044  

 9 765  

 7 866  

 12 203  

 11 577  

 7 880  

 7 523  

 15 077  

 9 858  

 13 513  

 8 136 

 9 380  

 6 507  

 8 569  

 9 989  

 10 034  

 13 680  

Note: Green coloured values denote cost approximation based on historical data at the level of operations. 

Source: prepared by the study team. 

Summary table 

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Definition of the SCO 

This SCO would cover the cost of one innovation voucher issued to an SME for 
completing innovation / digitisation activities. SMEs should obtain services from 

research organisations and other service providers which are accredited by the 

National Accreditation Authority in the respective Member State. 

Eligible activities 

• 

Provision of consultancy services provided to SMEs by universities, 

research centres or knowledge-intensive companies 

•  Consulting services for development of technical applications, 
technology transfer and technical know-how, techno-economic 
studies; 

•  Development of digital capabilities of the SME 
• 

Etc.  

Target group for the 

beneficiary 

Small  (from  10  to  49  employees)  and  medium  (from  50  to  249  employees) 
enterprises  

Indicator name  

Innovation voucher issued to SMEs to complete innovation / digitisation activities 

Measurement unit for 

the indicator  

Number of innovation vouchers issues to SMEs which have completed innovation 
/ digitisation activities. The measurement of this indicator should closely follow the 

definition  of  the  SCO  and  the  monitoring  framework  of  the  common  ERDF/CF 
indicator  ‘Number  of  enterprises  receiving  support  in  forms  of  non-refundable 

direct financial support conditional only to completion of project (grants)’. 

Base calculation 

formula 

𝑺𝑪𝑶𝟐𝑩 =

𝑪𝒕𝒐𝒕𝒂𝒍

𝒕𝒕𝒗𝒐𝒖𝒄𝒉𝒆𝒓𝒔

 , where 

ttvouchers is the number of innovation vouchers issued to SMEs, in units 

Ctotal is total costs of relevant operations, in EUR 

Eligible costs  

All eligible costs of the operation. 

Arrangements for the 

Proof of eligibility of the beneficiary 

audit trail 

•  HR documents of the SME, such as annual reports, confirming the 

size of the company is within the definition of an SME; 

Proof of accreditation of the service provider 

•  Accreditation of the private or public sector knowledge provider 
based on the National Accreditation Authority in the respective 
Member State; 

Proof that eligible activities have taken place 

Examples of documents which should include the requested information: 

• 

Programme guidelines established at the beginning of the voucher 

set-up process; 

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•  Documentation of the knowledge provider's involvement, including a 

description of the services provided; 

Service contract between research institution and SME; 
Progress reports or other project-related documentation; 

• 
• 
•  Documentation of any changes or deviations from the original project 

plan. 

Proof that the results delivered by projects are verifiable and real 

Examples of documents which should include the requested information: 

Technical specifications;  
Technology roadmaps; 

•  Reports on activities facilitated in the innovation voucher; 
• 
• 
•  White paper; 
•  Draft product; 
• 
•  Results of specific analysis.  

Process prototypes; 

Key risks/limitations 

Risk / limitation 1: ‘Locked’ 

Member States have discretion in 

and measures to 
prevent perverse 

incentives  

interventions / path dependence, i.e. it 
is assumed that Member States 

selecting the activities they intend to 
facilitate – they will be reimbursed 

historically implementing small- / 
large-scale interventions will continue 

to implement small- / large-scale 

interventions. 

through the same single SCO rate. 

Risk / limitation 2: SCO estimates are 

Statistical inference allows lump sum 

feasible only for Member States that 
provided the study with appropriate 

values to be established for all 
Member States based on statistical 

and sufficient historical data. 

indices (see Scenario 2). 

Risk / limitation 3: Beneficiaries may try 

The documentation in the audit trail 

to  misrepresent  expenses,  such  as  by 
claiming  expenses  for  activities  that 

will require that eligible activities are 
clearly described in order to claim 

were  not  actually  undertaken  or 
claiming  expenses  that  are  not  allowed 

under  the  terms  and  conditions  of  the 

voucher. 

reimbursement. 

The audit trail leaves flexibility to 

undertake activities which are eligible 
under the classification of innovation 

core activities (Com.Reg 995/2012) – 
activities outside of this classification 

will be deemed ineligible.  

Method for regular 
adjustment of the 

rates  

SCO values can be adjusted regularly on the basis of the following indicators: 

Labour cost index in professional, scientific and technical activities (Eurostat, 

LC_LCI_R2_A). Percentage change compared to previous year.  

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Harmonised index of consumer prices (HICP) in industrial goods, services and 

communication (Eurostat, PRC_HICP_AIND). Annual average rate of change. 

2.2.3.  Sub-area 2C: Research Development & Innovation Research Projects 

The core objective of Research Development & Innovation research projects is to facilitate and enhance 
collaboration between SMEs and public / private research institutions. New innovations are often built 
on new and varied sources of information and shared knowledge, and stem from collaboration efforts.  
Since SMEs are some  of the primary drivers of innovation, they can benefit greatly from such inter-
sectoral  linkages.  Such  collaboration  may  result  in  various  outputs,  not  limited  to  research  projects, 
new products to the market and joint academic publications. Initially, based on the data availability, 
quality and granularity, the study team considered two scenarios in sub-area 2C: 

- 
- 

Scenario A: an EU-level SCO based on historical data; 
Scenario B: an EU-level SCO merged with other sub-areas. 

After  carefully  assessing  both  scenarios,  the  study  team  concluded  that  neither  scenarios  could  be 
developed due to the data limitations in the collected historical data sample. The initial desk research 
and piloting demonstrated that the Member States implement a wide variety of interventions in this 
sub-area,  both  thematically  and  in  scope.  Unlike  in  other  sub-areas,  we  could  not  pre-fill  the  data 
collection form with activities since we could not identify the most common supported activities before 
launching historical data collection in all Member States. The historical data collection confirmed our 
initial understanding – even though most Member States submitted the historical data for sub-area 2C, 
it was very heterogenous and, as such, incomparable. It was evident from the sample that the Member 
States implemented very different activities across  and within countries, recorded different outputs, 
cost categories and cost breakdown. The  sections below provide  a detailed  overview of the  initially 
proposed scenarios, their advantages and the data limitations and challenges that precluded the study 
team from developing EU-level SCO for sub-area 2C.  

Scenario A  

An SCO that was considered for Scenario A was defined as a lump sum for the purpose of reimbursing 
one  Research  and  Innovation  project  implemented  in  collaboration  between  private  /  public 
research institution and an SME. The research projects covered under this SCO included two main 
cost items – costs related to industrial research and costs related to experimental development. It also 
included a number of enterprises that cooperate with research institutions in R&D projects, with at least 
one enterprise and one research institution participating in the project. The condition established was 
that one or more of the cooperating parties (research institution or enterprise) may receive the support, 
but it must be conditional on the cooperation. The cooperation may have been either new or existing 
but should have lasted at least for the duration of the project.   

The choice to base this provisional lump sum on industrial research and experimental development 
was informed by the historical data sample – these activities were the most commonly occurring among 
Member States (reported in 11 Member States from the sample). The main outputs that were considered 
to serve as the main indicator were the following: 

•  Number of enterprises cooperating with research institutions (CO26) 
•  Private investment matching public support (CO27) 

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The  advantage  of  such  a  lump  sum  is  that  it  reflects  the  collaboration  between  SMEs  and  research 
institutions  and  narrows  down  supported  activities  in  an  otherwise  significantly  varied  sample. 
However,  despite  the  potential  relevance  of  this  SCO,  this  Scenario  has  been  discarded  due  to 
numerous caveats in the historical data which did not allow the study team to calculate accurate and 
comparable SCO values.  

As  demonstrated  by  the  data  quality  assessment,  while  the  vast  majority  of  Member  States  (25) 
provided historical data for sub-area 2C on RDI Research projects, these data were very heterogeneous 
across  all  data  points,  including  supported  activities,  cost  categories  and  outputs.  Member  States 
recorded over 70 different outputs in this sub-area, with many of them not tracking data on the common 
indicators.  The  study  team  also  observed  a  very  large  variation  among  the  activities,  outlined  in 
Annex 3. Most of the supported activities were unique entries, ranging from… 

… broad categories:  

•  development of new products and solutions in the collaboration between enterprises and 

public research institutions (DK); 

•  projects contributing to the transition to a low-carbon economy (NL); 
• 
support for enterprises planning to start or develop R&D activity (PL) 

… to highly specific projects, such as: 

•  development of a vestibular implant for patients with failure of the vestibular system 

(AT); 

•  modernisation or upgrading of infrastructure, including deliveries of parts of research 

infrastructures or research equipment to research infrastructures abroad – materially and 
technically modernise large research infrastructures (CZ); 

•  development of a mobile 3D scanning room for passive high-speed scanning (DE). 

This  uniqueness  complicated  defining  a  SCO  because  it  failed  to  properly  reflect  all  the  different 
activities  Member  States  implement  and  even  if  values  could  have  been  extrapolated  from  a  small 
sample, there was a high likelihood that such an SCO would not be relevant for many Member States. 
In addition, the data were insufficiently granular in many cases, with 12 Member States not providing 
a total cost breakdown at activity level.  

The study team attempted to mitigate this heterogeneity by narrowing down the historical data sample 
by the most common outputs and activities. However, the sample size of countries that implemented 
industrial research / experimental development and tracked the outputs mentioned above on research 
collaboration  was  limited  to  only  6  Member  States.  As  such,  the  actual  scope  and  results  of  such 
collaborations were difficult to establish based on the available historical data.  

Another output that was considered for this lump sum was Private investment matching the public 
funding  (CO27),  which  was  the  most  common  output  among  Member  States  (17).  This  indicator 
captures private investment made by enterprises towards innovation activities and is measured in EUR 
matching public support in innovation or R&D projects. However, data analysis demonstrated that a) 
the  share  of  this  private  investment  varies  dramatically  among  Member  States  and  b)  this  is  not  a 
reliable  indicator  to  prove  that  the  collaboration  between  research  organisations  and  SMEs  was 
occurring since it only covers the private investment of the SME. With all of these limitations in mind, 
the study team also anticipated a difficult audit trail since a broad definition of this lump sum could 
result in perverse incentives.  

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Scenario B  

An EU-level SCO merged with other sub-areas was also considered since the data in sub-area 2C are 
too varied for developing an EU-level SCO. The study team explored the possibility of utilising the 
collected historical data by merging it with historical data from sub-areas 2A and 2B. Upon reviewing 
the  available  data,  the  study  team  discovered  that  Scenario  B  was  largely  not  feasible.  The  data 
provided in sub-area 2C covered a very large number of activities and outputs but did not have many 
overlaps  with  other  sub-areas.  It  yielded  limited  results,  such  as  additional  project  information  for 
Denmark and Latvia in the case of sub-area 2B. However, merging such data on a larger scale would 
have increased the risk of inaccurate values since Member States implemented different activities and 
incurred different costs in these sub-areas.  

As discussed above, since the study team still managed to collect a large sample of data, the option of 
merging  sub-area  2C  with  sub-area  2A:  Knowledge  /  technology  transfer  activities  of  SMEs  was 
considered because the initial data review revealed that there are some overlapping activities and cost 
categories within the historical data sample in those sub-areas. Specifically, the study team explored 
the possibility of merging data from Alternative B in sub-area 2A: Unit cost for one month of gross new 
working position (that did not exist before) to directly perform R&D activities in the SME.  

To check if data are comparable, the study team narrowed down the historical data sample in sub-area 
2C to only include projects and calls that monitored ERDF common indicator CO24: Number of new 
researchers in supported entities. The new sample included historical data on projects and calls from 
12 Member States (BE, CY, CZ, DE, FR, GR, HR, IT, LV, PL, RO, SI). Following a more thorough data 
review, it was discovered that available data are still insufficiently granular in some cases, with some 
Member States not providing data on the cost categories covered by the SCO in sub-area 2A Alternative 
B, namely Staff costs, Research / training costs, Management and indirect costs and Overheads. More 
specifically, Belgium, Greece, Cyprus, Latvia and Romania had sufficiently detailed cost breakdowns 
by category, while other Member States did not: 

• 

• 

In Slovenia, the only cost category that the data were provided for was Other costs (Costs 
of outsourcing, Flat rate financing determined by applying a percentage to one or more 
specified cost categories, Standard scales of unit costs).  
In Poland, Italy and France, the available cost breakdown by cost category consisted 
mainly of Staff costs and Costs of scientific and technical equipment and materials, as well 
as Other costs in PL and Presentations in FR. Only some projects in IT contain this cost 
breakdown, but many did not.  

•  Germany provided a detailed cost breakdown by category for some projects, while other 

projects contained only staff costs.  
In Croatia and Czechia, the cost breakdown was not available. 

• 

The data on activities also varied significantly across Member States and within Member States: 

• 

• 

In Slovenia, the activities mainly included: Research and development projects: industrial 
research (TRL 3-4) and experimental development (TRL 5-6), aimed at research and 
development of new technological solutions and new products, services and processes. 
In Poland, some of the main activities were indicated as targeted projects of SMEs 
including R&D works with implementation, Projects involving industrial research and 
development or development work, Infrastructure and equipment for laboratories or 
research and development departments in enterprises and other. 

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• 

• 
• 

• 

In Romania, some of the activities included Research and development activities 
(industrial research and/or experimental development), Procurement of R&D services 
(industrial research and/or development experimental development), Procurement of 
innovation advisory services relating to: technological assistance; technology transfer; 
acquisition, protection and commercialisation of industrial property rights; use of 
standards, Procurement of innovation support services related to: trials and testing in 
laboratories; quality marking, testing and certification; market studies and other. 
In Belgium, the supported activities contained Health care improvements (1), TIC (2).  
In Greece, the supported activities included industrial research, experimental 
development, feasibility studies, SMEs for participation in fairs, Innovation aid for SMEs. 
In Italy, the supported activities varied across the data sample, primarily specified as 
thematic research areas, such as a) Security and monitoring of the territory, networking / 
Smart city, environment and ecosystem, consolidation / relaunch of the existing industry, 
b) Consolidation / relaunch of the existing industry, c) Sustainable buildings, green 
buildings, and other.  

2.3.  Area 3: SCOs for SME Growth & Competitiveness 

The nature of the operation implemented under Area 3 limited the potential for developing alternative 
calculation approaches. Additionally, since a significant majority of Member States provided data on 
the same indicators, it did not encourage the creation of diverse options, thus the study team referred 
to lump sums which would be applicable to the whole operation in question. 

For sub-area 3A, a further approach was explored since the study team identified the distance from the 
home country as a crucial variable impacting the cost of participating in international events abroad. 
To address this, location-specific information on the events was asked for in the data collection form 
with  the  aim  of  attempting  to  develop  two  different  lump  sums:  one  for  international  events  in  EU 
countries and another for events outside of the EU. 

Unfortunately, not all Member States were able to provide location-specific data for all projects. For 
instance, some projects funded participation in multiple events or had several SMEs participating in 
different international events, making it difficult to disaggregate the information. As a matter of fact, 
out of the six MS that provided location-specific data (ES, FR, HR, IT, PL, and PT), IT and PL funded 
exclusively  participation  in  international  events  within  the  EU  and,  in  some  instances,  ES  and  HR 
reported aggregated data for multiple events, specifying the name of all the locations reached, but not 
providing a cost amount for each event. 

The  analysis  conducted  using  this  limited  set  of  data  revealed  no  correlation  between  the  cost  of 
participation in an event and the distance from the MS in question. This finding was also confirmed 
when examining SCO schemes submitted by CZ, where no correlation was observed. 

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Table 13. Member States which provided information on the location of the international event for 
sub-area 3A 

MEMBER STATE 

PROVIDED INFO ON EU 

DESTINATION 

PROVIDED INFO ON EXTRA 

EU+PROXIMITY 

DESTINATION 

CZ 

ES 

FR 

HR 

IT 

PL 

PT 

SI 

TOTAL 

Source: prepared by the study team. 

NO 

YES 

YES 

YES 

YES 

YES 

YES 

NO 

6 

NO 

YES 

YES 

YES 

NO 

NO 

YES 

NO 

4 

2.3.1.  Sub-area 3A: Networking Activities of SMEs 

International trade is a critical driver of the EU economy, supporting more than 90 million jobs through 
exports. Despite their significance, SMEs do not contribute to international trade to the same extent as 
larger  companies,  accounting  for  only  30 %  of  total  exports  (by  value)  to  non-EU  countries31. 
Furthermore,  the  increasing  internationalisation  of  business  relations  poses  numerous  risks  and 
challenges for SMEs. To mitigate these uncertainties, it is vital for SMEs to establish and maintain strong 
network relations.  

Participating in professional fairs and exhibitions is a valuable tool for SMEs to identify new markets 
and opportunities, establish links with strategic partners, and enhance growth. This approach allows 
them to remain competitive in a globalised economy. 

In the context of projects funded by the ERDF and CF the following activities were funded: 

•  Participation in trade fairs; 
•  Participation in international scientific conferences; 
•  Participation in partnership exchanges. 

To promote the internationalisation of SMEs and simplify their participation in networking events, we 
propose implementing the following EU-level SCO solution: 

31 European Court of Auditors (2022): Special Report:  SME internationalisation instruments -–A large number of support actions but not fully 
coherent or coordinated. Available at: https://op.europa.eu/webpub/eca/special-reports/sme-internationalisation-instruments-07-2022/en/  (Last 
accessed on 02.05.2023) 

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-  Lump sum to cover the cost of one SME attending a single international event (such as a 

trade fair, international conference, or partnership exchange). 

Definition of the SCO 

The proposed output-based SCO will cover the expenses of one SME attending a single international 
event, regardless of whether it is an EU or extra-EU event. The intended beneficiaries of this scheme 
are  the  SMEs  themselves.  To  receive  reimbursement,  Managing  Authorities  are  required  to  provide 
evidence, in their audit trail, that the SME participated in the event. 

Assessment of data 

The base data for this study were obtained from Managing Authorities and Intermediate Bodies in a 
standardised and structured data collection form. The data collected from Member States are based on 
verified expenditures from ERDF/CF. For sub-area 3A, 8 Member States (CZ, ES, FR, HR, IT, PL, PT, SI) 
provided data on completed projects. The data included the total verified expenditure, the number of 
SMEs supported by each project / call, and the number of events attended by the SMEs. 

The data provided by these MS refer to projects covering this set of eligible costs:  

• 
travel and accommodation; 
• 
transportation of materials; 
• 
costs of the stands (renting space and construction); 
•  promotion (e.g. production of booklets for the exhibition); 
• 
• 
• 

registration to the fair / event;  
staff costs; 
‘external’ specialised services (e.g. translation costs). 

As  the  study  team  considered  the  distance  of  the  event's  destination  as  a  possible  cost  factor,  their 
original plan was to create two separate cost options for these operations: a fixed amount for events 
held within the  EU and one  for  those held outside  the  EU. However, detailed information on event 
destinations was only available from a limited number of Member States that funded both EU and non-
EU events (ES, FR, HR, PT). Furthermore, in some MS (ES and HR), data were presented at a higher 
level of aggregation than the individual event, meaning that several events in different locations were 
funded  under  a  single  project,  making  it  impossible  to  determine  the  cost  of  each  event.  After 
conducting a statistical analysis using the limited data available, no significant correlation was found 
between the  distance of the  country and the  cost of the  operation. As a result,  it was not feasible  to 
establish two distinct SCOs for events held within the EU and those held outside of it. 

Calculation method 

Step  1 involved cleaning the available historical data provided by CZ, ES, FR, HR, IT, PL, PT, SI 
based  on  qualitative  criteria.  Projects  that  were  irrelevant  or  did  not  contain  all  the  necessary  data 
entries were excluded from further analysis. 

Step  2  involved  identifying  and  removing  outliers  from  the  data  provided  by  the  eight  Member 
States  (CZ,  ES,  FR,  HR,  IT,  PL,  PT,  SI)  that  provided  all  the  necessary  information.  This  was 
accomplished by calculating the average ratio between the total verified costs and the total number of 
events attended by an individual SME. Any values falling below the 10th percentile or above the 90th 
percentile of this average were considered outliers and therefore excluded. In the case of Member States 

73 

 
 
 
 
 
 
 
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with fewer than four records (such as CZ and SI), it was not possible to analyse the outliers. As such, 
these Member States required the SCO to be extrapolated through statistical sources (as outlined in Step 
3). 

Table 14. Networking activities of SMEs (Sub-Area 3A): identification and exclusion of the outliers 

MEMBER 
STATE 

RELEVANT DATA ON COMPLETED 
PROJECTS WITH INFO ON N. OF EVENTS 
PER SME 

N. OF OUTLIERS IDENTIFIED 

CZ 

ES 

FR 

HR 

IT 

PL 

PT 

SI 

3 

105 

4 

37 

6 

9 

691 

2 

n/a 

22 

2 

8 

2 

2 

138 

n/a 

Source: prepared by the study team. 

Step 3 involved the calculation of the total SCO for the six Member States (ES, FR, HR, IT, PL, PT) 
that provided all the necessary data. We calculated the average ratio between the total verified costs 
and  the  total  number  of  events  attended  by  an  individual  SME.  This  calculation  was  based  on  the 
database that had been cleared of any outliers. 

Step 4 involved adjusting the established rates to account for inflation. Since the rates were based on 
historical data for the period of 2014-2020, the estimates obtained in Step 3 needed to be updated to 
reflect  current  prices  in  2023.  To  achieve  this,  proxy  rates  were  indexed  based  on  the  average 
cumulative inflation of the Harmonised Indices of Consumer Prices from 2014 to 2022.  

Step 5 involved extrapolating rates for the remaining 21 Member States.  To do so, the rates for the 
total cost of  the  interventions  were determined  through linear  regression. The labour  cost levels for 
services to the business economy in 2020 were used as the independent (predictor) variable. 

Rates 

Table 15. Proposed SCO rates for sub-area 3A 

MEMBER STATE 

TOTAL (€), PER SME ATTENDING ONE INTERNATIONAL 
EVENT 

AT 

BE 

BG 

 13 596  

 14 045  

 10 816  

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MEMBER STATE 

TOTAL (€), PER SME ATTENDING ONE INTERNATIONAL 
EVENT 

CY 

CZ 

DE 

DK 

EE 

ES 

FI 

FR 

GR 

HR 

HU 

IE 

IT 

LT 

LU 

LV 

MT 

NL 

PL 

PT 

RO 

SE 

SI 

SK 

 11 628  

 11 514   

 13 443   

 14 618   

 11 475   

 12 049   

 13 395   

 14 933   

 11 857   

 10 357   

 11 198   

 13 023   

 11 174   

 11 160   

 14 560   

 11 237   

 11 666   

 13 443   

 12 118   

 12 405   

 10 969   

 13 959   

 12 077   

 11 447   

Summary table 

Definition of 

This output-based SCO would cover the cost of one SME attending one international 

the SCO 

event. 

Eligible 

activities 

•  Participation to trade fairs 
•  Participation to international scientific conferences 
•  Participation to partnership exchanges 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

Target group for 

the beneficiary 

Small (from 10 to 49 employees) and medium (from 50 to 249 employees) enterprises  

Indicator name  

Number of SMEs attending one international event 

Measurement 

Number  of  SMEs  attending  one  international  event  in  units.  The  measurement  of  this 

unit for the 

indicator  

indicator should closely follow the definition of the SCO. 

Base calculation 

𝑺𝑪𝑶𝟑𝑨 =

𝑪𝒕𝒐𝒕𝒂𝒍

𝒕𝑺𝑴𝑬∗𝒕𝑬𝒗𝒆𝒏𝒕

 , where 

formula 

tSME is the total number of SMEs supported 

tEvent is the total number of event attended by a single SME 

Ctotal is total costs of relevant activities, in EUR 

Eligible costs  

All eligible costs of the operation. 

Arrangements 

Proof that eligible activities have taken place 

for the audit 

trail 

•  Registration to the fairs. 
•  Documents  providing  evidence  of  actual  attendance  to  the  international 

event such as: photographic evidence, exhibitors catalogue, etc. 

Key 

Risk / limitation 1: Beneficiaries may aim to 

The selection procedure should aim to 

risks/limitations 
and measures to 

minimise their expenditure towards 
fulfilling the condition while undertaking 

award only projects with clear added 

value. 

prevent 
perverse 

incentives  

a low-value project. 

Risk / limitation 2: SCO estimates are 
feasible only to Member States that 

Statistical inference allows to establish 
unit cost values for all Member States 

provided the study with appropriate and 

based on cost indices.  

sufficient historical data. 

Method for 

SCO values can be adjusted regularly on the basis of the following indicator: 

regular 
adjustment of 

the rates  

Harmonised Indices of Consumer Prices (HICPs). Percentage change compared to 

previous year.  

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2.3.2.  Sub-area 3B: Consultancy / advisory services to elaborate an 

internationalisation strategy 

SMEs face more challenges compared to their larger counterparts in fully  utilising the opportunities 
provided by the  Single  Market,  competing in the  global arena, navigating financial crises,  attracting 
skilled  personnel,  and  securing  investments.  It  is  acknowledged  that  SMEs  often  lack  the  internal 
resources and know-how needed to conduct research that can aid them in making informed decisions 
while seeking investment opportunities and evaluating their potential for growth and competitiveness. 
This limitation leads to a significant dependence on external consulting services. 

Providing entrepreneurs with the required insights in a timely manner through consultancy services 
can  help  them  initiate  necessary  actions  to  ensure  their  future  growth  and  survival.  Therefore,  it  is 
important to provide easier access to external consultancy services to address the expertise gap within 
SMEs.  Research  suggests  that  internationalisation  consultancy  can  significantly  reduce  the  barriers 
faced  by  SMEs  during  the  internationalisation  process,  especially  those  related  to  knowledge  and 
networks  of  internationalisation  and  management32.  By  utilising  the  knowledge  and  experience  of 
internationalisation consultants, SMEs can overcome their limitations and minimise the risks they face. 

To  facilitate  easier  access  to  consultancy  services  for  the  development  of  an  internationalisation 
strategy, we propose implementing the following EU-level SCO solution: 

-  Lump sum to cover the costs of an SME seeking consultancy or advisory services to 

develop an internationalisation strategy. 

Definition of the SCO 

This output-based SCO aims to cover the average cost incurred by one SME for seeking consultancy / 
advisory services to develop an internationalisation strategy. The main beneficiaries of this scheme are 
SMEs. To claim reimbursement, Managing Authorities need to provide evidence in their audit trail that 
the consultancy / advisory service has been utilised by the SME. 

Assessment of data 

The base data for this study was obtained from Managing Authorities and Intermediate Bodies in a 
standardised and structured data collection form. The data collected from Member States was based on 
verified expenditures from ERDF/CF. For sub-area 3B, 9 Member States (BE, CZ, ES, HU, IT, PL, PT, 
SE,  SI)  provided  relevant  data  on  completed  projects,  specifying  both  the  total  amount  of  verified 
expenditures,  the  number  of  SMEs  supported  by  each  project  /  call  and  the  number  of  consultancy 
advisory services supported. 

The data provided by these MS refer to projects covering different categories of eligible costs:  

•  direct costs other than staff (e.g. external consultancy services, travel, trademarks, 

acquisition of information, equipment, etc.); 
staff costs; 

• 

32Pöyhtäri, J.P. (2019): The role of internationalisation consultancy in SME internationalisation. Available at: 
https://jyx.jyu.fi/bitstream/handle/123456789/65251/1/URN%3ANBN%3Afi%3Ajyu-201908163854.pdf. Last access: 2 May 2023. 

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• 

indirect costs. 

Calculation method 

Step 1 involved cleaning the available historical data provided by BE, CZ, ES, HU, IT, PL, PT, SE, SI 
based  on  qualitative  criteria.  Projects  that  were  irrelevant  or  did  not  contain  all  the  necessary  data 
entries were excluded from further analysis. 

Step 2 involved identifying and removing outliers from the data provided by the nine Member States 
(BE,  CZ,  ES,  HU,  IT,  PL,  PT,  SE,  SI)  that  have  provided  all  the  necessary  information.  This  was 
accomplished by calculating the average ratio between the total verified costs and the total number of 
SMEs having elaborated an internationalisation strategy. Any values falling below the 10th percentile 
or above the 90th percentile of this average are considered outliers and therefore excluded. In the case 
of Member States with fewer than four records (such as CZ, IT, SI, SE), it was not possible to analyse 
the outliers. As such, these Member States require the SCO to be extrapolated through statistical sources 
(as outlined in Step 3). 

Table 16. Consultancy/advisory services to elaborate an internationalisation strategy (sub-area 3A): 
identification and exclusion of the outliers 

MEMBER 
STATE 

RELEVANT DATA ON COMPLETED 
PROJECTS WITH INFO ON N. OF 
SERVICES PER SME 

N. OF OUTLIERS IDENTIFIED 

BE 

CZ 

ES 

HU 

IT 

PL 

PT 

SI 

SE 

9 

2 

34 

5 

2 

7 

1188 

2 

2 

2 

n/a 

4 

2 

n/a 

2 

238 

n/a 

n/a 

Source: prepared by the study team. 

Step 3 involved the calculation of the total SCO for the five Member States (BE, ES, HU, PL, PT) that 
have provided all the necessary data. We calculated the average ratio between the total verified costs 
and the total number of SMEs having elaborated an internationalisation strategy. This calculation was 
based on the database that has been cleared of any outliers. 

Step 4 involves adjusting the established rates to account for inflation. Since the rates were based on 
historical data from the period of 2014-2020, the estimates obtained in Step 3 needed to be updated to 
reflect  current  prices  in  2023.  To  achieve  this,  proxy  rates  were  indexed  based  on  the  average 
cumulative inflation of the Harmonised Indices of Consumer Prices from 2014 to 2022.  

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Step 5 involved extrapolating rates for the remaining 22 Member States.  To do so, the rates for the 
total cost of  the  interventions were  determined  through linear  regression. The labour  cost levels for 
services to the business economy in 2020 were used as the independent (predictor) variable. 

Rates 

MEMBER STATE 

TOTAL (€), PER SME ATTENDING ONE INTERNATIONAL 
EVENT 

AT 

BE 

BG 

CY 

CZ 

DE 

DK 

EE 

ES 

FI 

FR 

GR 

HR 

HU 

IE 

IT 

LT 

LU 

LV 

MT 

NL 

PL 

PT 

RO 

SE 

SI 

SK 

 8 921   

9 125  

 7 636   

 8 011   

 7 958   

 8 850   

 9 393   

 7 941   

8 403  

 8 828   

 9 000   

 8 117   

 7 821   

7 516  

 8 656   

 8 620   

 7 795   

 9 366   

 7 830   

 8 029   

 8 850   

8 196  

7 835  

 7 707   

 9 088   

 8 219   

 7 927   

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Summary table 

Definition of 

This output-based SCO would reflect the average cost incurred by an SME for 

the SCO 

consultancy/advisory services to develop an internationalisation strategy. 

Eligible 

activities 

Target group for 

the beneficiary 

Elaboration of an internationalisation strategy for an SME through a consultancy 

activity. 

Small (from 10 to 49 employees) and medium (from 50 to 249 employees) enterprises.  

Indicator name  

Number of SMEs that have elaborated an internationalisation strategy. 

Measurement 

The total number of SMEs that have elaborated an internationalisation strategy in units. 

unit for the 

indicator  

The measurement of this indicator should closely follow the definition of the SCO. 

Base calculation 

𝑺𝑪𝑶𝟑𝑩 =

formula 

𝑪𝒕𝒐𝒕𝒂𝒍

𝒕𝑺𝑴𝑬∗𝒕𝒔𝒕𝒓𝒂𝒕𝒆𝒈𝒚

 , where 

tSME is the total number of SMEs supported 

tstrategy is the total number of internationalisation strategies elaborated by a single SME 

Ctotal is total costs of relevant activities, in EUR 

Eligible costs  

All eligible costs of the operation. 

Arrangements 

Proof that eligible activities have taken place 

for the audit 

trail 

Final report of the consultancy service carried out (activities performed, results 

obtained…). 

Key 
risks/limitations 

Risk / limitation 1: Beneficiaries may aim to 
minimise their expenditure towards 

The selection procedure should aim to 
award only projects with clear added 

and measures to 
prevent 

perverse 

incentives  

fulfilling the condition while undertaking a 

value. 

low-value project. 

Risk / limitation 2: SCO estimates are 

Statistical inference allows unit cost 

feasible only for Member States that 
provided the study with appropriate and 

values to be established for all Member 

States based on cost indices.  

sufficient historical data. 

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Method for 
regular 

adjustment of 

the rates  

SCO values can be adjusted regularly on the basis of the following indicator: 

Harmonised Indices of Consumer Prices. Percentage change compared to previous year.  

2.4. 

Feasibility checks 

2.4.1.  FC1: Promoting sustainable multimodal urban mobility 

In  accordance  with  the  policy  objectives  set  out  in  Article 5(1)  of  Regulation  (EU) 2021/1060,  the 
ERDF/CF  shall  support,  among  others,  the  objective  of  contributing  to  ‘a  greener,  low-carbon 
transitioning  towards  a  net  zero  carbon  economy  and  resilient  Europe  by  promoting  clean  and  fair 
energy  transition,  green  and  blue  investment,  the  circular  economy,  climate  change  mitigation  and 
adaptation, risk prevention and management, and sustainable urban mobility’ (Policy Objective 2).   

In this feasibility check, we focus on one of the eight specific objectives of Policy Objective 2, which is 
promoting  sustainable  multimodal  urban  mobility,  as  part  of  the  transition  to  a  net  zero  carbon 
economy (RSO2.8).  

The  section  is  structured  as  follows.  First,  we  present  the  main  findings,  detailing  the  operations 
considered for the result-based tools, as well as potential indicators and data sources that could be used 
to establish the amounts linked to achieving the results. Second, we present an overview of the potential 
FNLC in the selected area, showing the most promising alternatives. 

2.4.1.1  Operations considered for result-based tools 

The  first  step  of  the  analysis  conducted  by  the  research  team  was  to  review,  classify  and  define 
operations  considered  for  result-based  tools.  By  examining  the  available  information  on  109 
programmes planned for the period of 2021-2027 across 21 Member States, funded by either the ERDF 
or the CF (according to the Dataset ‘2021-2027 Finances details’ of the European Commission Cohesion 
Open  Data  Platform)33,  we  observed  that  programmes  categorised  under  the  sustainable  urban 
mobility sub-area encompass a total of 24 intervention fields. Below we list the most common ones, 
i.e. fields covered by programmes in three or more Member States: 

clean urban transport infrastructure, present in 21 Member States; 
cycling infrastructure, present in 19 Member States; 
clean urban transport rolling stock, present in 15 Member States; 
alternative fuels infrastructure, present in 15 Member States; 

• 
• 
• 
• 
•  digitalisation of transport when dedicated in part to greenhouse gas emissions reduction: 

urban transport, present in 12 Member States; 

•  digitalisation of urban transport, present in 12 Member States; 
• 

air quality and noise reduction measures, present in 8 Member States. 

33 Available at: https://cohesiondata.ec.europa.eu/2021-2027-Categorisation/2021-2027-Finances-details-categorisation-multi-fu/hgyj-gyin. Last 
access: 6 Apr 2023. 

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In  Table  17,  we  illustrate  potential  interventions  in  the  area  by  presenting  three  examples  of 
programmes related to the sub-area of sustainable urban mobility from three different countries (Spain, 
Portugal, and Belgium). 

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Table 17. Examples of supported operations / projects in the area of sustainable urban mobility 

PROGRAMME 

SUPPORTED OPERATIONS / PROJECTS 

Canarias (Spain) 

• 

• 

• 

Facilitating  internal  and  external  access  to  collective  transport  stations,  improving  the  possibilities  of 

transporting  bicycles  on  urban  and  interurban  public  transport,  installing  bicycle  rental  or  public  bicycle 

services, and launching campaigns to promote the combined use of bicycles and public transport. 

Conserving  and improving  existing  public  transport infrastructure  to  efficiently  meet  mobility  demand, as 

well as optimising the use of the infrastructure through demand management measures. 

Promoting energy efficiency in public transport infrastructure and facilities. 

•  Developing  pilot  programmes  for  the  study  and  implementation  of  demand  management  measures,  the 

development of an integrated information and management system for urban / interurban public transport, 

improving its connectivity with other modes of transport and adopting homogeneous ticket systems between 

different urban areas, standardising the collection and treatment of basic transport data, or developing urban 

transport systems with reserved platforms, among others. 

Algarve 

Regional 

• 

Connection in a dedicated public transport system between Faro-Aeroporto-Universidade do Algarve-Parque 

Programme 

(Portugal) 

das Cidade-Loulé-Olhão, supporting the largest population concentration in the south of the country. 

Sustainable Urban Logistics Plans and other studies supporting the planned interventions. 

Creation of logistics spaces and systems for restriction and control of loading and unloading. 

Promotion of multimodal logistics interfaces. 

Creation / requalification of regional cycling structure and isolated pedestrian and cycling areas in each urban 

• 

• 

• 

• 

centre. 

•  Management systems for circulation and parking that prioritise soft modes and public transport. 

•  Development of smart mobility solutions and integrated information platforms. 

Flanders (Belgium) 

• 

• 

• 

• 

Flexibilisation of transport systems by adapting services to less densely populated areas. 

Further  roll-out  of  mobi  points  as  a  contribution  to  the  modal  shift  and  development  of  alternative    /  

sustainable  modes  of  mobility,  e.g.  by  providing  adapted  transfer  infrastructure,  bicycle  parking  facilities, 

charging stations, among others. 

Pilot and demo projects that promote multimodality. 

Further roll-out of bicycle highways. 

INTERVENTION FIELDS 

•  Air  quality  and  noise 

reduction 

measures 

• 

• 

• 

Clean urban transport infrastructure 

Clean urban transport rolling stock 

Cycling infrastructure 

•  Digitalisation of urban transport 

•  Digitalisation  of  urban  transport  -–

GHG emission reduction 

•  Alternative fuels infrastructure 

• 

• 

• 

Clean urban transport infrastructure 

Clean urban transport rolling stock 

Cycling infrastructure 

•  Digitalisation of urban transport 

•  Digitalisation  of  urban  transport  – 

GHG emission reduction 

• 

• 

Clean urban transport infrastructure 

Cycling infrastructure 

•  Digitalisation  of  urban  transport  -–

GHG emission reduction 

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PROGRAMME 

SUPPORTED OPERATIONS / PROJECTS 

INTERVENTION FIELDS 

• 

Pilot projects on Intelligent Transport Systems: Integrating information and communication technologies in 

road transport with other modes of transport (smart data-driven traffic management). 

•  Multimodal  infrastructure  projects  specifically  aimed  at  the  transition  from  trucks  to  more  sustainable 

• 

• 

• 

• 

• 

• 

infrastructure such as rail and inland shipping. 

Local  distribution  projects  such  as  delivery  with  smaller,  electric  vehicles;  more  efficient  loading  and 

unloading, delivery by water, among others. 

Projects  that  improve  the  preconditions  for  green  mobility  (e.g.  electric,  hydrogen-based  on  renewable 

energy), aimed at making public transport more accessible. 

Removing the barriers that stand in the way of the transition to sustainable modes of transport. 

Pilot  projects  on  the  Internet  of  Things,  e.g.  devices  /  sensors  to  improve  route  planning  in  the  context  of 

mobility, increase the efficiency of public transport, optimise traffic light schedules, variable speed limits, etc. 

Innovative projects that convert big data into tailor-made smart data. 

Projects that improve both the connection between the mobility networks and modes internally in Flanders 

and the connection of  the  Flemish  mobility  networks  with those in  the  neighbouring regions at  home  and 

abroad. 

Source: Dataset ‘2021-2027 Finances details’, complemented by official programme documentation.

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After reviewing the programmes listed in the table above, it becomes evident that projects in this area 
may  have  a  broad  scope,  including  operations  in  multiple  fields  of  intervention  simultaneously. 
However, despite the heterogeneity of intervention fields within the selected sub-area, we also see a 
significant  overlap  between  the  operations  supported  in  the  analysed  programmes.  For  example, 
looking at the list of 109 current programmes under ERDF/CF funds, we  observe that almost half of 
them cover, at the same time, projects related to the three main intervention fields listed previously, i.e. 
clean  urban  transport  infrastructure  (present  in  73 %  of  programmes  in  this  area),  cycling 
infrastructure (82 % of programmes) and urban transport rolling stock (65 % of programmes)(Error! 
Reference source not found.). 

Figure 2. Overlap of intervention fields in the sub-area of sustainable urban mobility 

Source: Prepared by the study team, based on the dataset ‘2021-2027 Finances details’. 

Considering  the  weight  of  these  three  intervention  fields,  the  next  sections  of  the  present  feasibility 
check will focus on potential FNLC(s) for investment in cycling infrastructure, clean urban transport 
rolling stock and clean urban transport infrastructure.  

2.4.1.2  Result indicators considered for result-based tools 

In this  section,  we elaborate  on  potential result  indicators considered for  the  development  of FNLC 
solutions. Error! Reference source not found. illustrates the intervention logic for the urban mobility 
field, showcasing the expected outcomes and results of funded operations in the investment area under 
examination  and  focusing  on  operations  related  to  clean  urban  transport  infrastructure  (including 
rolling stock), as well as cycling infrastructure. The elaboration of outputs, results, and impacts listed 

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in the figure is inspired by the European Parliament and Council's Regulation (EU) 2021/1058, which 
defines common indicators to assess each specific objective for the ERDF and the CF34. 

Figure 3. Sustainable urban mobility intervention logic 

Source: Prepared by the study team based on Regulation (EU) 2021/1058. 

When identifying indicators to serve as a base for developing FNLC solutions, a valuable resource is 
the  set  of common indicators used by the  European  Regional  Development Fund and the  Cohesion 
Fund.  These  indicators  are  deemed  relevant  as  they  are  grounded  in  the  primary  strategies  of  the 
European Union, and also allow for comparisons across different programmes and Member States.  

The  main  source  of  the  most  recent  information  on  common  ERDF/CF  result  indicators  is  national 
agents, such as programme  managing authorities. However, initial information about the  indicators 
that are being tracked in the current period can be found in the dataset ‘2021-2027 Achievement Details’. 
The  latter  is  available  on  the  European  Commission  Cohesion  Open  Data  Platform35.  The  dataset 
contains  data  on  the  indicators  for  measuring  the  performance  of  adopted  2021-2027  programmes, 
including information on both common and specific output and result measures. It currently  provides 
information on the initially planned indicator values. However, from 2023 onwards, it is intended to 
also incorporate time-series information on targets, as well as cumulative amounts reported for decided 
and implemented values. 

Based  on  the  above-mentioned  dataset,  we  identified  the  following  result-based  indicators  being 
tracked during the 2021-2027 period by projects funded by ERDF/CF in sustainable urban mobility: 

• 
• 
• 

annual users of dedicated cycling infrastructure (RCR64); 
annual users of new or modernised public transport (RCR62); 
annual users of new or modernised tram and metro lines (RCR63); 

34 Available at: https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:02021R1058-20210630&qid=1681300139712&from=en. Last 
access: 13 Apr 2023. 
35 Available at: https://cohesiondata.ec.europa.eu/2021-2027-Indicators/2021-2027-Achievement-Details-multi-funds-/xi3a-zddk. Last access: 12 
Apr 2023. 

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estimated greenhouse emissions (RCR29); 
jobs created in supported entities (RCR01); 

• 
• 
•  population benefiting from measures for air quality (RCR50); 
•  private investments matching public support (of which: grants, financial instruments) 

(RCR02); 
small and medium-sized enterprises introducing product or process innovation (RCR03); 

• 
•  users of new and upgraded public digital services, products and processes (RCR11). 

Table 18 details the number of Member States tracking each indicator mentioned above. We can observe 
that the most used common ERDF/CF indicators in the current period for programmes on sustainable 
urban mobility are: annual users of dedicated cycling infrastructure (RCR64), which is being tracked 
by 17 Member States; annual users of new or modernised public transport (RCR62), which is tracked 
by 15 Member States; annual users of new or modernised tram and metro lines (RCR63), tracked by 
10 Member States; and estimated greenhouse emissions (RCR29), tracked by 13 Member States. Three 
of these indicators (see Table 19 for further details) are directly related to operations discussed in the 
previous  section,  i.e.  clean  urban  transport  infrastructure  (related  to  indicator  RCR62),  clean  urban 
transport rolling stock (related to RCR63) and cycling infrastructure (related to RCR64). 

Table 18. Common ERDF/CF indicators in the area of sustainable urban mobility 

RCR64 

RCR62 

RCR63 

RCR29 

RCR01 

RCR50 

RCR02 

RCR03 

RCR11 

BE 

CY 

CZ 

DE 

EE 

EL 

ES 

FR 

HR 

HU 

IT 

LT 

LV 

MT 

PL 

PT 

RO 

SE 

SI 

SK 

Total 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

17 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

15 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

10 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

13 

1 

2 

1 

X 

1 

Source: Prepared by the study team, using the dataset ‘2021-2027 Achievement Details’. 

X 

X 

2 

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Table 19. Overview of selected indicators in the area of sustainable urban mobility 

INDICATOR 

INDICATOR 

MEASUREMENT 

DEFINITION AND CONCEPTS 

CODE 

NAME 

UNIT 

RCR62 

Annual users of 
new or 

modernised 

public transport 

users/year 

Annual users of new or modernised public transport financed by supported 
projects. Public transport covers urban and suburban, such as bus, trolley bus, 

water bus lines (which are not tram, metro – see RCR63). Modernisation of public 
transport refers to significant improvements in terms of infrastructure, and access 

and quality of service. 

TIME MEASUREMENT 

ACHIEVED 

One year after the 
completion of output in 

the supported project. 

RCR63 

Annual users of 

users/year 

Annual users of new or modernised tram and metro lines financed by supported 

One year after the 

new or 
modernised tram 

and metro lines 

projects. The indicator also covers urban and suburban rail lines. Modernisation of 
these transport services refers to significant improvements in terms of 

completion of output in 

the supported project. 

infrastructure, and access and quality of service. Urban and suburban rail lines 
refer to networks that are functionally separate from the rest of the railway system 

and intended only for the operation of local, urban or suburban passenger 

services. 

RCR64 

Annual users of 

users/year 

Annual users of dedicated cycling infrastructure financed by supported projects. 

One year after the 

dedicated cycling 

infrastructure 

Dedicated cycling infrastructure includes cycling facilities separated from roads 
for vehicular traffic or other parts of the same road by structural means (kerbs, 

completion of output in 

the supported project. 

barriers), cycling streets, cycling tunnels, etc. 

Source: Metadata of common ERDF/CF/JTF indicators36.

36 Available at: https://cohesiondata.ec.europa.eu/2021-2027-Indicators/2021-2027-ERDF-CF-JTF-Common-Indicators/4t73-mihb. Last access: 13 Apr 2023. 

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Based on desk research, we provide our assessment of each of the selected result-based indicators based 
on the criteria detailed in Table 20. Our assessment is presented in Table 21. 

Table 20. Criteria for feasibility assessment 

CRITERIA 

DESCRIPTION 

Relevance  

Robustness 

Practicality  & 
perversity 

Relevance  of  the  results  indicator  towards  main  EU  strategies,  based  on  desk 
research and/or consultation with Member States.  

Relates  to  the  available  data,  its  completeness  and  comprehensiveness.  This 
presumes  that  all  data  points  needed  for  calculations  and  establishment  of  a 
particular  FNLC  are  sufficiently  covered  in  the  data  sample,  whereas  the  latter 
provides a reasonable coverage of Member States. Incomplete data (i.e. provisional 
/  estimated data or data gaps) are as dangerous as inaccurate data. Gaps in data 
lead to a partial view of the overall picture. Without a complete picture, FNLC may 
be calculated through uninformed actions. 

Relates to the administrative burden of the prospective arrangements for the audit 
trail  and  the  documentation  required  to  verify  that  results  have  been  achieved 
and/or  conditions  have  been  met.  Administrative  burden  level  depends  on  the 
scope,  detail  and  accessibility  of  evidence  required  to  validate  or  invalidate  the 
achievement of results. It also relates to the possibility of undesirable effects of the 
FNLC, risks of perverse incentives or unintended negative effects of applying the 
proposed  FNLC  solution  (such  as  creaming  /  cherry-picking  in  the  selection  of 
participants37 or parking38). 

Source: Prepared by the study team. 

Table 21. Assessment of selected indicators in the area of sustainable urban mobility 

CRITERIA 

Relevance 

DESCRIPTION 

These common ERDF/CF indicators are  overall relevant, as they are rooted in the 
main EU strategies, being directly related to the area of sustainable urban mobility. 
More precisely: 

•  Annual  users  of  new  or  modernised  public  transport  is  related  to  the 

intervention field of clean urban transport infrastructure. 

•  Annual users of new or modernised tram and metro lines is related to the 

intervention field of clean urban transport rolling stock. 

•  Annual  users  of  dedicated  cycling  infrastructure  is  related  to  the 

intervention field of cycling infrastructure.  

Robustness 

Based  on  desk  research  and  information  available  on  programmes  being 
implemented  in  the  2021-27  period  considering  20  Member  States  providing 

37 The act of creaming and cherry-picking involves choosing participants who are the easiest to help, to ensure that providers can meet the desired 
outcomes. 
38 Parking is a practice where providers attempt to minimise costs by providing minimal assistance to those with the lowest anticipated outcomes, 
while concentrating their resources on clients who have better prospects. 

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CRITERIA 

DESCRIPTION 

information on indicators in the area of sustainable urban mobility, the result-based 
indicators on: 

•  Annual  users  of  new  or  modernised  public  transport  is  tracked  by  15 
Member States in the current period, indicating high comprehensiveness of 
the indicator.   

•  Annual users of new or modernised tram and metro lines is tracked by 10 
indicating  moderate 

current  period, 

the 

in 

Member 
comprehensiveness of the indicator. 

States 

Practicality & 
perversity 

•  Annual users of dedicated cycling infrastructure is tracked by 17 Member 
States  in  the  current  period,  indicating  high  comprehensiveness  of  the 
indicator. 

At  the  moment,  the  study  team  does  not  have  access  to  the  collected  data  from 
countries in order to verify its completeness / quality. The information will be fact-
checked after further consultations with the Member States. 

An FNLC based on annual users of new or modernised public transport or annual 
users of new or modernised tram and metro lines should not be administratively 
burdensome,  as  access  to  information  regarding  the  number  of  users  should  be 
readily  available  upon  request  from  institutions  responsible  for  ticket  sales  and 
access control, such as data on purchases and use of gates / turnstiles.  

However, in the case of the annual users of dedicated cycling infrastructure, there 
is  a  challenge  associated  with  accurately  monitoring  the  number  of  new  users  of 
cycling infrastructure in a way that is audit-friendly. This challenge can be mitigated 
if the project is accompanied by implementing an automated way to count cyclists 
(such as by implementing radars). This solution, however, may increase the cost of 
interventions in this area. 

Perverse incentives such as creaming and cherry-picking are reduced for the three 
indicators, but still present. Beneficiaries might, for instance, choose to implement 
projects in more populated areas where there are already other types of sustainable 
transport available, in order to receive a larger outcome-based payment, therefore 
neglecting  certain  areas.  For  instance,  in  the  case  of  cycling  infrastructure, 
beneficiaries may focus on areas where there are already a high number of cyclists, 
only to receive a larger outcome-based payment, therefore neglecting areas where 
cycling infrastructure could be more needed (e.g. areas with low coverage of buses 
and trains). In the case of public transport, beneficiaries may also offer services at 
lower  fares  only  to  increase  temporarily  the  number  of  users.  Requesting 
information on available modes of sustainable transport in the area, as well as their 
prices, can help reduce this type of perverse incentive. Additionally, there is also a 
risk  that  beneficiaries  focus  more  on  increasing  the  number  of  users  than  on  the 
quality of the  service provided. In that regard, the  implementation of satisfaction 
surveys can help monitor the quality of the service and decrease the risk.  

Although  all  the  three  indicators  measure  overall  usage  without  considering 
differences  in  transport  mode  users,  the  risk  of  parking  is  still  present  if 
infrastructure  construction  regulations  do  not  require  accommodations  for 
individuals with disabilities. To mitigate this risk, a reimbursement condition based 
on infrastructure adaptations for individuals with disabilities could be added. 

It  is  important  to  note  that  the  mitigation  measures  mentioned  above  are  merely 
advisory and thus do not require inclusion in the audit trail within a potential EU-

90 

 
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CRITERIA 

DESCRIPTION 

level FNLC scheme. However, these measures could potentially be implemented at 
the programme level in due course. 

Source: Prepared by the study team. 

2.4.1.3  Amounts linked to achievement of results 

Concerning  establishing  financial  amounts  for  the  selected  indicators,  the  study  team  revised  four 
potential data sources. 

The first (and the most feasible) is the publication ‘Handbook on the external costs of transport’39, the 
result  of  a  study  commissioned  by  the  European  Commission.  It  provides  an  overview  of  the 
methodologies  and  input  values  which  can  be  used  to  estimate  all  main  external  costs  of  transport. 
External costs (or externalities), in this report, are understood as the result of actions of one individual 
or  group  on  another  individual  or  group  that  generate  an  impact  that  is  not  accounted  for  or 
compensated by the responsible party. The external transport costs reflect the difference between social 
costs, which include all expenses related to the provision and use of transport infrastructure, and the 
private costs directly incurred by the transport user. 

The study provides figures for total, average and marginal external transport costs in all EU countries. 
Financial  figures  are  expressed  in  Euro  price  levels  of  2016,  with  average  and  marginal  amounts 
expressed in Euro-cent per PKM (Passengers-Kilometres)40. Calculations of external costs consider all 
main externalities of transport: accidents; air pollution; climate change; noise; congestion; well-to-tank 
emissions;  habitat  damage;  other  external  cost  categories  (e.g.  soil  and  water  pollution).  In  this 
handbook, transport infrastructure costs are not considered, as they are part of another study (which 
will also be discussed below).  

Among other modes, the study considers road (passenger car, motorcycle, bus, coach, light commercial 
vehicles  and  heavy  goods  vehicles)  and  rail  transport  (high-speed  passenger  train,  passenger  train 
electric, passenger train diesel, freight train electric and freight train diesel). Thus, values in this report 
can  be  a  potential  source  of  financial  information  related  to  indicators:  Annual  users  of  new  or 
modernised public transport and Annual users of new or modernised tram and metro lines. 

One  advantage  of  this  study  is  that,  while  the  first  Handbook  was  published  in  2008,  it  has  been 
updated twice ever since (once in 2014 and again in 2019). However, it is not clear if new versions of 
the report will be published in future years. Another advantage is its level of detail. The handbook has 
a  detailed  explanation  of  the  methodology  used  for  each  cost  category,  and  also  provides  an  Excel 
Annex with all values used for the calculations included in the report. This high level of detail makes 
reproducibility easier should the study not be updated. The main disadvantage of this study, however, 
is that values for different modes of public transport are not divided into those with lower or higher 
emissions. For instance, even though they differentiate between electric and diesel passenger trains, the 
same  differentiation  does  not  happen  with  buses  and  coaches.  Thus,  final  calculations  may 
overestimate  the  external  costs  if  we  are  interested  in  only  the  more  sustainable  types  of  passenger 
mobility. Still, from all the data sources evaluated by the study team, this study was identified as the 

39 Available at: https://op.europa.eu/en/publication-detail/-/publication/9781f65f-8448-11ea-bf12-01aa75ed71a1. Last access: 14 Apr 2023. 
40 PKM is a unit of measurement representing the transport of one passenger over one kilometre. 

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one with the most potential to be used for estimating financial values for indicators related to  public 
transport. 

A complementary source to the study above is the publication ‘Overview of transport infrastructure 
expenditures  and  costs’41,  also  commissioned  by  the  EC.  The  study  presents  the  estimates  of  total, 
average and marginal infrastructure costs for several modes of transport, including road (passenger 
car,  motorcycle,  bus,  coach,  van  and  heavy  goods  vehicle)  and  rail  (high-speed  passenger  train, 
passenger train electric, passenger train diesel, freight train electric and freight train diesel), in all EU 
countries.  

In the study, transport infrastructure is understood as the physical and organisational network which 
allows movements between different locations. Road transport infrastructure considers costs such as 
land, roadworks prior to paving, pavement and ancillary works, engineering structures, traffic signs 
and signalling and telecommunications installations, lighting installations, toll collection installations, 
buildings,  energy,  vehicles,  etc.,  used  by  the  infrastructure  department,  traffic  management  and 
cleaning icy / snowy roads. Rail transport infrastructure considers costs such as ground area, track and 
track  bed,  platforms,  engineering  structures,  level  crossings,  superstructure,  access  ways,  safety, 
signalling and telecommunications and lighting installations. Average costs are expressed as the costs 
per transport performance unit (e.g. Euro per PKM) based on total annualised costs (considering both 
depreciation and financing costs). 

Although  valuable,  it  is  important  to  point  out  some  limitations  of  using  the  latter  study.  The  first 
drawback is that there may be country differences in the values used to calculate costs, explained by 
potential differences in the scope of the data for each country. For instance, expenditures on a certain 
category may not (or only partly) be included in the available data, reducing the comparability of these 
data between countries. The other limitation is the lack of frequent updates to the report. In addition, 
the  study  uses  several  statistical  techniques  to  estimate  missing  data  (such  as  extrapolation  and 
interpolation), so final values may not be completely accurate. Last, it is important to note that the study 
focuses  only  on  expenditures  and  costs,  using  information  not  only  from  international  aggregated 
sources, such as the Organization for Economic Co-operation and Development (OECD) and Eurostat, 
but also using historical data on expenditures collected from national sources. Therefore, although it 
can be used as a potential source of data for triangulation, it is not recommended as a unique reference 
for the development of a FNLC solution. 

We also considered the guidebook ‘Transportation Cost and Benefit Analysis: Techniques, Estimates 
and  Implications’42,  developed  by  the  Victoria  Transport  Policy  Institute,  which  provides  detailed 
information on transportation economic impacts (such as benefits and costs), analysing how costs and 
benefits vary for different transportation modes and conditions. Besides the monetised estimates for 
each type of cost (e.g. travel time, parking, congestion, safety / health, air pollution, noise etc), in US$ 
per  passenger-mile,  for  11  travel  modes,  it  has  an  estimation  of  the  average  external  cost  savings 
(reductions  in  vehicle  costs,  congestion,  parking,  roadway  costs,  etc.)  due  to  a  shift  from  the  use  of 
average  car  travel  to  another  mode  (such  as  compact  fuel-efficient  car,  electric  car,  bicycle,  electric 
bus/trolley etc). However, after further analysis, the study team considered this data source unsuitable 
due to its severe limitations, listed below. 

First,  even  though  it  provides  figures  more  related  to  sustainable  mobility  than  the  other  studies 
identified by the study team (disaggregating the costs of 11 different modes of transport and covering 

41 Available at: https://op.europa.eu/en/publication-detail/-/publication/7ab899d1-a45e-11e9-9d01-01aa75ed71a1. Last access: 14 Apr 2023. 
42 Available at: https://www.vtpi.org/tca. Last access: 14 Apr 2023.  

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23 cost categories), the values currently provided in this report are outdated, with the last update of 
final calculations dating back to 2009. Second, cost categories are based on data not only from European 
countries but also those from other regions of the world, such as North America and Oceania, with final 
values being provided at the aggregate level and not always based on the same list of countries. The 
study team believes that if this study is not updated with more recent figures, taking into account the 
technological changes that happened in the previous decade and providing more disaggregation for 
European countries, using this data source would be unfeasible. 

Last,  another  potential  source  of  financial  information  is  the  report  ‘The  EU  cycling  economy’43, 
produced by the European Cyclists’ Federation and published in 2016. The report is an update of its 
first version published in 2013, and it estimates the economic benefits of cycling in European countries. 
The  report  calculates  benefits  based  on  an  extensive  list  of  factors,  such  as  climate,  environment, 
environmental  asset  development,  energy,  resources,  direct  health  benefits,  road  safety  /  reduced 
accidents,  health  economic  benefits,  EU  bike  industry,  bicycle  and  parts  sales  and  repairs,  bicycle 
tourism, road safety, urban design, smarter cycling, quality of time spent cycling, shopping by bike, 
child welfare, quality of space, social affairs, mobility, road infrastructure and diversity of (cycling-) 
cultures. Estimated benefits of cycling are given in billion euro based on a value of 134 billion kilometres 
cycled  per  year  for  the  EU-28.  One  advantage  is  that  the  report  specifies  the  data  sources  used  to 
estimate each factor, which facilitates reproducibility. It also specifies which values are calculated based 
on concrete evidence, which are based on the best available data and which are based on estimations 
using the best available indicators. 

Although valuable, this study has some significant limitations. One is that calculations are based on the 
latest  available  data,  which  can  be  from  different  years,  so  techniques  to  harmonise  the  data  are 
required. Second, values are presented only at the aggregated level, so statistical techniques to estimate 
data  points  for  each  Member  State  are  also  needed.  Third,  values  are  presented  in  total  euro,  so 
information on the number of cyclists will be necessary in order to estimate unit values based on this 
report. Given the amount of statistical techniques necessary to determine different amounts for each 
Member State based on this study, we believe using this data source as the main source of financial 
values  would  significantly  reduce  the  accuracy  of  the  results,  making  its  use  less  viable.  Table  22 
provides an overview of the potential data sources discussed in this section. 

43 Available at: https://www.ecf.com/system/files/THE_EU_CYCLING_ECONOMY.pdf. Last access: 18 Apr 2023. 

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Table 22. Overview of potential data sources to establish financial amounts in the area of sustainable urban mobility 

POTENTIAL DATA 

RELATED 

SOURCE(S) 

INDICATOR(S) 

ADVANTAGES 

LIMITATIONS 

‘Handbook on the 
external costs of 

Annual users of new 
or modernised public 

Presents external costs in euro-PKM for 
several modes of transport, including 

No disaggregation between modes of 

transport with lower or higher emissions.  

transport’ 

transport. 

road and rail. 

It is unclear if there will be future 

Annual users of new 

Provides estimates for all EU countries.  

updates to the reports. 

or modernised tram 

and metro lines. 

Consider all main externalities of 

transport. 

Highly detailed.  

‘Overview of 

transport 
infrastructure 

expenditures and 

costs’ 

Annual users of new 

Presents infrastructure costs in euro-

Lack of frequent updates.  

or modernised public 

PKM for several modes of transport, 

transport. 

including road and rail. 

No disaggregation between modes of 

transport with lower or higher emissions. 

Annual users of new 
or modernised tram 

and metro lines. 

Provides estimates for all EU countries.  

Potential country differences in the 

Potential source for triangulation. 

values used to calculate costs. 

Potential inaccuracy of values, due to the 
extensive use of statistical techniques to 

estimate missing data. 

Focus only on expenditures and costs. 

‘The EU cycling 

Annual users of 

Estimates the economic benefits of 

Unclear if there will be future updates to 

economy’ 

dedicated cycling 

infrastructure. 

cycling in European countries in billion 
euro based on a value of 134 billion 

kilometres cycled per year for the EU-

28. 

the report. 

Values used in this report are not 

necessarily from the same year. 

Values are presented only at the 

Calculations  are  based  on  an  extensive 

aggregated level. 

list of factors. 

METHODS TO ADJUST THE 

AMOUNTS 

Figures in those reports are 
presented in Euro. Should there be 

no recent update, values from the 
latest reports can be updated using 

relevant price index figures from 
Eurostat (unit value for Member 

State X * index for Member State X), 
such as the Harmonised Index of 

Consumer Prices (annual average 
rate of change) and/or the Labour 

Cost Index (percentage change 
compared to previous year) in the 

relevant areas used to estimate 
transport costs in each data source 

(such as transport, health, and 

electricity / gas / fuels). 

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POTENTIAL DATA 

RELATED 

SOURCE(S) 

INDICATOR(S) 

ADVANTAGES 

LIMITATIONS 

METHODS TO ADJUST THE 

AMOUNTS 

Specifies the data sources for estimating 
facilitates 
each 

factor, 

which 

reproducibility. 

‘Transportation Cost 
and Benefit Analysis: 

Annual users of new 
or modernised public 

Presents monetised estimates in dollar-
passenger-mile for each type of 

Statistical techniques and additional data 
search will be required in order to 

estimate values for each cyclist based on 

this report. 

Outdated values. 

Final values presented in the aggregate 

level, also covering countries outside the 

Techniques, 
Estimates and 

Implications’ 

transport. 

transportation cost. 

Annual users of new 

High number of cost categories 

EU. 

or modernised tram 

covered. 

and metro lines.  

Annual users of 
dedicated cycling 

infrastructure. 

Easier disaggregation between modes 

of transport with lower or higher 

emissions. 

Source: Prepared by the study team.

Figures in this report are presented 
in US dollars, so final values 

should be adjusted to Euro using 
exchange rates. Although values in 

Euro can be updated to present 
values using relevant price index 

figures from Eurostat (as detailed 
above), it is possible that these 

price indices may not fully capture 
the impact of recent technology 

developments on prices, as these 
developments may have occurred 

after the last report was published. 

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2.4.1.4 

Feasibility assessment 

As established in earlier sections, the  potential  to develop a  FNLC solution(s) was assessed in three 
intervention fields related to sustainable urban mobility sub-area: clean urban transport infrastructure, 
cycling infrastructure and clean urban transport rolling stock. A total of 15 or more Member States 
support operations in at least one of these three fields, covering 98 % of programmes in the examined 
area with available information.  

We  evaluated  several  potential  result-based  indicators  being  tracked  by  Member  States  under  these 
interventions,  with  the  most  relevant  ones  being  annual  users  of  dedicated  cycling  infrastructure 
(RCR64), annual users of new or modernised public transport (RCR62), and annual users of new or 
modernised tram and metro lines (RCR63). 

Table 23 summarises the potential FNLC solutions identified by the research team for these indicators. 
For each solution, we also provide a description of operation types, results to be achieved, indicator 
name,  measurement  unit,  verification  mechanism,  key  risks  and  potential  data  sources  to  establish 
financial amounts. 

Table 23. Summary of potential FNLC solutions in the area of sustainable urban mobility 

DESCRIPTION 

COMPONENT 

CLEAN URBAN 

TRANSPORT 

INFRASTRUCTURE 

CLEAN URBAN TRANSPORT 

ROLLING STOCK 

CYCLING INFRASTRUCTURE 

Potential FNLC 

Target-based approach 

Target-based approach based on 

Target-based approach based on 

approach 

based on the number of new 

the number of new users of new or 

the number of new users of 

users of new or modernised 

modernised tram and metro lines. 

dedicated cycling infrastructure. 

public transport. 

Description of 

Operations targeting the 

Operations targeting the creation 

Operations targeting the creation 

the operation 

creation or modernisation of 

or modernisation of tram and 

of dedicated cycling 

type 

public transport, i.e. bus, 

metro lines, including urban and 

infrastructure, i.e. cycling 

trolley bus, water bus lines 

suburban rail lines (i.e. networks 

facilities separated from roads for 

but NOT tram and metro. 

that are functionally separate from 

vehicular traffic or other parts of 

Modernisation refers to 

the rest of the railway system and 

the same road by structural 

significant improvements in 

intended only for the operation of 

means (kerbs, barriers), cycling 

infrastructure, access and 

local, urban or suburban 

streets, cycling tunnels, etc. 

quality of service. 

passenger services). 

Modernisation refers to significant 

improvements in infrastructure, 

access and quality of service.  

Description of 

The release of funds is linked to the following outcome being achieved: verified number of new users of 

results to be 

[new or modernised public transport / new or modernised tram and metro lines / dedicated cycling 

achieved with a 

infrastructure] as a result of supported projects. As an example, the release of funds could be contingent 

timeline 

upon reaching a specific number of users within a designated timeframe. For instance, achieving an 

increase of X users by 2025 and Y users by 2028. Alternatively, targets can also be set as percentages, 

e.g. increase in X% in the number of users (compared to the baseline values) by 2025 and in Y% by 2028. 

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COMPONENT 

CLEAN URBAN 

TRANSPORT 

INFRASTRUCTURE 

CLEAN URBAN TRANSPORT 

ROLLING STOCK 

CYCLING INFRASTRUCTURE 

DESCRIPTION 

It is worth noting that although certain milestones could be established, the study team currently does 

not have any specific recommendations regarding the most optimum milestones and timeframe that 

would balance the simplification and financial sustainability aspects of the proposed FNLC solution. 

Our intention is to engage in discussions with Member States during the upcoming workshop in order 

to evaluate the possibility of developing more concrete and tangible proposals. 

Indicator name  Number of new users of 

Number of new users of new or 

Number of new users of 

new or modernised public 

modernised tram and metro lines. 

dedicated cycling infrastructure. 

transport. 

If measured as percentage: 

If measured as percentage: 

If measured as percentage: 

Increase of users of new or 

Increase of users of dedicated 

Increase of users of new or 

modernised tram and metro lines. 

cycling infrastructure. 

modernised public 

transport. 

Measurement 

Number of new users (persons) = Number of users (current) – Number of users (baseline) 

unit 

Increase of new users (%) = [Number of new users Number of users (baseline)] * 100 

Verification 

Document justifying how the target(s) was satisfactorily fulfilled. 

Document justifying how the 

mechanism 

E.g. proof of transport use, such as data on ticket purchases, 

target(s) was satisfactorily 

and/or use of gates / turnstiles. 

fulfilled. E.g. proof of transport 

use, such as data from bicycle 

radars. 

Key risks and 

Beneficiaries may opt to execute projects in densely populated regions where other sustainable 

measures to 

transportation options are already available, to receive a higher outcome-based payment, potentially 

prevent them 

disregarding other areas. They may also offer services at lower fares with the intention of temporarily 

increasing the number of users. To counteract such unintended consequences, requesting information 

regarding available modes of sustainable transport and their respective prices in the area can be useful. 

Another possible concern is that beneficiaries may prioritise increasing the number of users over 

providing high-quality services. To address this, satisfaction surveys can be implemented to monitor 

service quality and minimise such risks. 

The risk of parking is present if infrastructure construction regulations do not require accommodations 

for individuals with disabilities. To alleviate this, a condition that offers reimbursement based on 

infrastructure adaptations for individuals with disabilities can be added. 

The aforementioned mitigation measures serve as advisory recommendations and, as such, may not 

necessarily need to be documented in the audit trail for a potential EU-Level FNLC scheme. However, 

they could potentially be implemented at the programme level in due course. 

Potential data 

From all the data sources evaluated by the study team, the study “ 

The report ‘The EU cycling 

sources to 

establish 

financial 

amounts 

‘Handbook on the external costs of transport’ was identified as the 

one with the most potential to be used for estimating financial 

values for indicators related to urban transport, as it provides 

figures for external transport costs in all EU countries, expressed in 

Euro-pkm. A complementary source that can potentially be used 

for triangulation is the study ‘Overview of transport infrastructure 

expenditures and costs’, which presents estimation on 

infrastructure costs and expenditures for all European countries. 

economy’ estimates the economic 

benefits of cycling in European 

countries. Using this source, 

however, has several limitations, 

such as requiring extensive use of 

statistical techniques to estimate 

data points for each Member 

State (reducing the accuracy of 

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COMPONENT 

CLEAN URBAN 

TRANSPORT 

INFRASTRUCTURE 

CLEAN URBAN TRANSPORT 

ROLLING STOCK 

CYCLING INFRASTRUCTURE 

DESCRIPTION 

We also considered the guidebook ‘Transportation Cost and 

the estimated values) and 

Benefit Analysis: Techniques, Estimates and Implications’. 

requiring additional data 

However, after further assessment, its use was deemed less 

sources, making its use less 

feasible, given the amount of statistical techniques necessary to 

viable. 

determine different amounts for each Member State. 

Source: Prepared by the study team. 

2.4.2.  FC2: Skills for smart specialisation and transition 

2.4.2.1  Operations considered for result-based tools 

In  accordance  with  the  policy  objectives  set  out  in  Article 5(1)  of  Regulation  (EU) 2021/1060,  the 
ERDF/CF shall support, among others, the objective of contributing to ‘a more competitive and smarter 
Europe by promoting innovative and smart economic transformation and regional ICT connectivity’ 
(Policy Objective 1).   

In this feasibility check, we focus on one of the five specific objectives of Policy Objective 1, which is 
Skills for smart specialisation and transition  (RSO1.4). From this point onwards, our assessment is 
limited to this investment area. 

The  first  step  of  the  analysis  conducted  by  the  research  team  was  to  review,  classify  and  define 
operations  considered  for  result-based  tools.  By  examining  the  available  information  on  67 
programmes  planned  for  the  period  of  2021-2027  across  21  Member  States,  funded  by  the  ERDF 
(according  to  the  Dataset  ‘2021-2027  Finances  details’  of  the  European  Commission  Cohesion  Open 
Data Platform44), we observed that skills for smart specialisation and transition sub-area encompass a 
total  of  16  intervention  fields.  The  most  frequently  encountered  intervention  field  is  'Skills 
development for smart specialisation, industrial transition, entrepreneurship, and adaptability of 
enterprises  to change'. This field  is addressed by programmes  in 20 Member  States.  The remaining 
intervention fields have been adopted by three or fewer Member States and are therefore not covered 
by this feasibility check. 

Table 24. Examples of supported operations / projects in the area of skills for smart specialisation 
and transition 

PROGRAMME 

SUPPORTED OPERATIONS / PROJECTS 

INTERVENTION FIELDS 

Programme for 

• 

To develop the skills of employees needed by SMEs, 

• 

Skills development for 

the European 

Union funds’ 

investments in 

allowing them to adapt to technological changes in 

the economy and industrial transformation. 

smart specialisation, 

industrial transition, 

entrepreneurship and 

44 Available at: https://cohesiondata.ec.europa.eu/2021-2027-Categorisation/2021-2027-Finances-details-categorisation-multi-fu/hgyj-gyin. Last 
access: 16 May 2023. 

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PROGRAMME 

SUPPORTED OPERATIONS / PROJECTS 

INTERVENTION FIELDS 

2021-2027 

(Lithuania) 

• 

To drive the transition to a knowledge-based and 

adaptability of enterprises 

higher value added economy and address the 

to change. 

challenges of industrial transformation planned 

• 

Research and innovation 

investments in human resources focused on specific 

processes, technology 

training of various forms and levels (upgrading and 

transfer and cooperation 

retraining), special attention for the development and 

between enterprises, 

improvement of digital skills of SME employees. 

research centres and 

• 

Strengthening scientific management and knowledge 

universities, focusing on 

commercialisation capacities in research and study 

the low-carbon economy, 

institutions: implementation of scientific 

resilience and adaptation 

management and knowledge commercialisation 

to climate change. 

capacities. 

NP Research, 

•  Development of a skilled workforce that is able to 

• 

Skills development for 

innovation and 

seize the opportunities arising from the dual green 

competitiveness 

and digital transition within companies. 

for green and 

•  Development of skills in applied research with 

digital transition 

industrial characterisation. 

smart specialisation, 

industrial transition, 

entrepreneurship and 

adaptability of enterprises 

2021-2027 (Italy) 

• 

Strengthening of skills for the active functioning of 

to change.  

the innovation ecosystem. 

•  Digitising SMEs or large 

enterprises (including e-

Commerce, e-Business and 

networked business 

processes, digital 

innovation hubs, living 

labs, web entrepreneurs 

and ICT start-ups, B2B) 

compliant with greenhouse 

gas emission reduction or 

energy efficiency criteria. 

Lisbon  Regional 

•  Develop the regional innovation ecosystem 

•  Development of skills for 

Programme 

(Portugal) 

implement strategies, programmes, and initiatives to 

smart specialisation, 

foster innovation, creating an ecosystem that 

industrial transition, 

supports collaboration, knowledge exchange, and the 

entrepreneurship and the 

development of new ideas and technologies.  

ability of companies to 

• 

Strengthen the linkages between higher education 

adapt to change. 

institutions and industry to ensure that education 

• 

Technology transfer and 

programmes align with the needs of the digital 

economy. Promote the integration of digital 

cooperation between 

enterprises, research 

technologies in various sectors, including education, 

centres and higher 

healthcare, manufacturing, and services, by 

education sector. 

providing training programmes, incentives, and 

• 

SME business 

infrastructure support. 

•  Assist industries in transitioning towards more 

advanced and sustainable models by providing 

development and 

internationalisation, 

including productive 

resources, expertise, and incentives. Encourage the 

investments.  

adoption of technologies that improve productivity, 

reduce environmental impact, and foster innovation. 

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PROGRAMME 

SUPPORTED OPERATIONS / PROJECTS 

INTERVENTION FIELDS 

Facilitate the development of industry clusters and 

networks to encourage collaboration and knowledge 

sharing among businesses. 

• 

Establish platforms and mechanisms to facilitate 

collaboration between businesses, academia, research 

institutions, and government entities. Encourage the 

sharing of knowledge, expertise, and resources to 

foster innovation and drive economic growth. 

Source: Dataset ‘2021-2027 Finances details’, complemented by official programme documentation. 

2.4.2.2  Result indicators considered for result-based tools 

In  this  section,  we  discuss  the  potential  result  indicators  considered  for  the  development  of  FNLC 
solutions. After analysing the programmes within the specific intervention field and taking into account 
the European Parliament and Council's Regulation (EU) 2021/1060, it becomes evident that the activities 
conducted  in  this  context  primarily  focus  on  training  and  apprenticeships.  These  activities  aim  to 
enhance the competencies of workers in various fields, such as smart specialisation, green transition, 
industrial  transition,  digital  transition,  upskilling,  reskilling,  collaboration  and  networking,  and 
technology  transfer.  The  objective  is  to  cultivate  a  skilled  workforce  capable  of  capitalising  on  the 
opportunities arising from the dual green and digital transition within companies. 

Figure  4 illustrates the  intervention logic  for  the  smart specialisation field, showcasing the  expected 
outcomes and results of funded operations in the investment area under examination and focusing on 
operations  related  to  skills  for  smart  specialisation,  industrial  transition,  entrepreneurship  and 
adaptability of enterprises to change. The elaboration of outputs, results, and impacts listed in the figure 
is  inspired  by  the  European  Parliament  and  Council's  Regulation  (EU)  2021/1058,  which  defines 
common indicators to assess each specific objective for the ERDF and the CF45.  

45 Available at: https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:02021R1058-20210630&qid=1681300139712&from=en. Last 
access: 13 Apr 2023. 

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Figure 4. Skills development for smart specialisation, industrial transition, entrepreneurship, and 
adaptability of enterprises to change intervention logic 

Source: Prepared by the study team based on Regulation (EU) 2021/1058. 

When  identifying  indicators  to  serve  as  a  possible  basis  for  developing  FNLC  solutions,  a  valuable 
resource is the set of common indicators used by the European Regional Development Fund and the 
Cohesion Fund. These indicators are deemed relevant as they are grounded in the primary strategies 
of the European Union, also allowing for comparisons across different programmes and Member States.  

The  main  source  of  the  most  recent  information  on  common  ERDF/CF  result  indicators  is  national 
agents, such as programme  managing authorities. However, initial information about the  indicators 
that are being tracked in the current period can be found in the dataset ‘2021-2027 Achievement Details’. 
The  latter  is  available  on  the  European  Commission  Cohesion  Open  Data  Platform46.  The  dataset 
contains  data  on  the  indicators  for  measuring  the  performance  of  adopted  2021-2027  programmes, 
including information on both common and specific output and result measures. It currently provides 
information on the initially planned indicator values. However, from 2023 onwards, it is intended to 
also incorporate time-series information on targets, as well as cumulative amounts reported for decided 
and implemented values. 

Based on the dataset mentioned above, we have identified the following result-based indicators that 
are being tracked by projects funded by ERDF/CF during the period of 2021-2027. These indicators were 
identified by filtering for programmes that financed projects under the specific objective RSO1.4, which 
focuses on skills for smart specialisation and transition. 

Jobs created in supported entities (RCR 01); 

1. 
2.  Private investments matching public support (RCR 02); 
3.  Small and medium-sized enterprises (SMEs) introducing product or process innovation 

(RCR 03); 

46 Available at: https://cohesiondata.ec.europa.eu/2021-2027-Indicators/2021-2027-Achievement-Details-multi-funds-/xi3a-zddk. Last access: 12 
Apr 2023. 

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4.  SMEs introducing marketing or organisational innovation (RCR 04); 
5.  Patent applications submitted (RCR 06); 
6.  New enterprises surviving in the market (RCR 17); 
7.  SMEs using incubator services after incubator creation (RCR 18); 
8.  Apprenticeships supported in SMEs (RCR 97); 
9.  SMEs staff completing training for skills for smart specialisation, for industrial transition 

and entrepreneurship (RCR 98). 

These nine indicators were assessed based on the following criteria: 

Table 25. Criteria for feasibility assessment 

CRITERIA 

DESCRIPTION 

Relevance  

Relevance  of  the  results  indicator  towards  main  EU  strategies,  based  on  desk 
research and consultation with Member States.  

Robustness 

Relates  to  the  available  data,  its  completeness  and  comprehensiveness.  This 
presumes that all data points needed for calculations and establishing a particular 
FNLC are sufficiently covered in the  data sample, whereas the  latter provides a 
reasonable  coverage  of  Member  States.  Incomplete  data  (i.e.  provisional  /  
estimated data or data gaps) are as dangerous as inaccurate data. Gaps in data lead 
to a partial view of the overall picture. Without a complete picture, FNLC may be 
calculated through uninformed actions. 

Practicality  & 
perversity 

Relates to the administrative burden of the prospective arrangements for the audit 
trail  and  the  documentation  required  to  verify  that  results  have  been  achieved 
and/or  conditions  have  been  met.  Administrative  burden  level  depends  on  the 
scope,  detail  and  accessibility  of  evidence  required  to  validate  or  invalidate  the 
achievement of results. It also relates to the possibility of undesirable effects of the 
FNLC, risks of perverse incentives or unintended negative effects of applying the 
proposed  FNLC  solution  (such  as  creaming  /  cherry-picking  in  the  selection  of 
participants or parking). 

Source: prepared by the study team. 

Regarding relevance, the two indicators that are most pertinent to operations typically funded in the 
field of ‘skills for smart specialisation and transition’ (as shown in Table 25) are: 

•  Apprenticeships supported in SMEs (RCR 97); 
• 

SMEs staff completing training for skills for smart specialisation, for industrial transition 
and entrepreneurship (RCR 98); 

Regarding robustness, we analysed the use of the nine indicators at the individual Member State 
level. As evident from the following table, the two indicators that are most commonly employed are:  

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• 

SMEs staff completing training for skills for smart specialisation, for industrial transition 
and entrepreneurship (RCR 98), which is being used by 14 Member States;  

•  Private investments matching public support (RCR 02) which is used by 7 Member States. 

Table 26. Common ERDF/CF indicators in the area of skills for smart specialisation and transition 

RCR01  RCR02  RCR03  RCR04  RCR06  RCR17  RCR18  RCR97  RCR98 

x 

x 

BE 

CY 

CZ 

DE 

EE 

EL 

ES 

FR 

HR 

HU 

IT 

LT 

LV 

MT 

PL 

PT 

RO 

SE 

SI 

SK 

Total 

2 

x 

x 

x 

x 

x 

x 

x 

x 

7 

x 

x 

x 

x 

x 

x 

x 

1 

1 

1 

1 

3 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

14 

x 

x 

x 

3 

Source: Prepared by the study team, using the dataset ‘2021-2027 Achievement Detail’ 

Based on the analysis of the relevance and robustness, the indicator  SMEs staff completing training for 
skills for smart specialisation, for industrial transition and entrepreneurship (RCR 98) appears to be the most 
suitable to represent a possible basis for developing FNLC. The following table summarises the key 
characteristics of this indicator. 

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Table 27. Overview of selected indicator in the area of skills for smart specialisation and transition 

INDICA

INDICATOR 

MEASUREM

DEFINITION AND CONCEPTS 

TIME 

TOR 

CODE 

NAME 

ENT UNIT 

MEASUREMENT 

ACHIEVED 

RCR98 

SMEs 

staff 

participants 

Number of participants from SMEs (including micro 

Upon completion of 

completing 

training 

for 

skills for smart 

specialisation, 

for 

industrial 

transition  and 

entrepreneurs

hip (by type of 

skill:  technical, 

management, 

entrepreneurs

hip, 

green, 

other). 

enterprises) who complete training / activity for skills 

activity 

for  skills 

development  for  smart  specialisation,  for  industrial 

development. 

transition  and  entrepreneurship.  The  types  of  skills 

include the following categories:  

- Technical skills: skills required for problem solving, 

design,  operation,  rethinking  and  maintenance  of 

machinery 

or 

technological 

structures, 

IT 

professional skills;  

-  Management  skills:  skills  relating  to  business 

planning,  complying  with  regulations  and  quality 

control, human resources planning, and allocation of 

resources;  

-  Entrepreneurial  skills:  specific  skills  for  start-up 

companies  such  as  risk  acceptance  /  management, 

strategic thinking and confidence, the ability to make 

personal  networks,  and  the  ability  to  deal  with 

challenges and requirements of different nature;  

-  Green  skills:  specific  skills  to  modify  products, 

services  or  operations  due  to  climate  change 

adjustments,  environmental  protection,  circular 

economy,  resource  efficiency  and  requirements  or 

regulations;  

-  Other  skills:  skills  other  than  the  four  types 

described above. 

Source: Metadata of common ERDF/CF/JTF indicators47. 

With regard to the third criterion listed in Table 25, namely practicality & perversity it is important to 
underline that an FNLC-based SMEs staff completing training for skills for smart specialisation, for industrial 
transition  and  entrepreneurship  (RCR  98)  should  not  be  administratively  burdensome,  as  access  to 
information  regarding  the  number  of  participants  should  be  readily  available  upon  request  from 
Managing Authorities.   

Perverse incentives such as creaming and cherry-picking should not be applicable to this indicator, as 
the achievement of the target is solely determined by the completion of activities for skills development. 
Nevertheless,  there  is  a  possibility  that  firms  may  be  motivated  to  rapidly  train  a  large  number  of 
employees, neglecting the effectiveness and relevance of the training. This can result in hastily executed 

47 Available at: https://cohesiondata.ec.europa.eu/2021-2027-Indicators/2021-2027-ERDF-CF-JTF-Common-Indicators/4t73-mihb. Last access: 16 
May 2023. 

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or superficial training programmes that fail to adequately address the specific needs of the employees 
or equip them with the essential skills for improved job performance. The emphasis on quantity may 
overshadow the significance of delivering high-quality and impactful training. 

2.4.2.3  Amounts linked to achievement of results  

To establish financial amounts for the selected indicator, ‘RCR98 – SMEs staff completing training for 
skills  for  smart  specialisation,  for  industrial  transition  and  entrepreneurship’  the  ‘Continuing 
Vocational  Training  Survey  (CVTS)’  carried  out  by  Eurostat  in  a  co-ordinated  form  in  all  the  EU 
Member States was identified as potential suitable source of data.  

The CVTS provides comparable data on ‘vocational training within EU enterprises that have at least 10 
or more employees and belong to specific economic activity groups’1. So far, six waves of the CVTS 
have been conducted in the following years: 1993, 1999, 2005, 2010, 2015, and 2020.   

It  is  worth  noting  that  the  expression  ‘Continuing  Vocational  Training’  (CVT)  refers  to  training 
initiatives or actions that aim primarily to acquire new skills or enhance existing ones. These activities 
require financial contribution from the enterprises, covering at least a portion of the expenses, for their 
employees.  Individuals  who  hold  apprenticeship  or  training  contracts  should  not  be  considered  for 
Continuing Vocational Training and it is crucial that the training initiatives or activities in question are 
pre-planned,  organised,  or  supported  with  the  specific  objective  of  learning2. Microenterprises  were 
not included in the survey. 

The survey covers various topics on the matter, including the provision of CVT courses and other forms 
of CVT (training / non-training enterprises), CVT strategies, participants in CVT courses, costs of CVT 
courses, time spent in CVT courses, characteristics of CVT courses, and assessment of CVT activities.   

Based on the data collected through the survey, two databases were created, which could be used as 
reliable proxies to estimate the cost incurred by enterprises for training an individual:  

• 

• 

'Cost of CVT courses by type and size class – cost per participant'48;  

'Cost  of  CVT  courses  by  type  and  size  class  –  cost  per  person  employed  in  enterprises 
providing CVT courses’49. 

The  first  database  is  the  'Cost  of  CVT  courses  by  type  and  size  class  –  cost  per  participant'.  This 
database provides an annual average cost per MS per participant for continued vocational training. It 
employs  the  same  unit  of  measurement  as  the  common  result  indicator,  specifically  the  number  of 
training participants. As a result, it can serve as a reliable proxy for the development of a scheme based 
on the RCR98 indicator.  

The second database is the 'Cost of CVT courses by type and size class – cost per person employed in 
all enterprises. It is worth noting that this database is not fully consistent with the definition of the 
indicator RCR98 presented in Table 27. It indeed presents data on the number of employees receiving 
training  over  a  year,  while  RCR98  is  based  on  the  number  of  participants  receiving  training.  In  the 
indicator's  logic,  if  a  person  participates  in  multiple  training  sessions,  each  participation  is  counted 

48 Database TRNG_CVT_19S, 
https://ec.europa.eu/eurostat/databrowser/view/TRNG_CVT_19S/default/table?lang=en&category=educ.educ_part.trng_cvt.trng_cvt_03  

49 Database TRNG_CVT_18S, 
https://ec.europa.eu/eurostat/databrowser/product/view/TRNG_CVT_18S?lang=en&category=educ.educ_part.trng_cvt.trng_cvt_03  

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separately, while in the database an employee participating in one or more training sessions is always 
counted as one. Therefore, this database can be used for the development of a different scheme based 
on the number of employees receiving trainings annually. 

Box 4. Participant vs Employed 

Participant 

It refers to a unique instance of an individual worker's participation in a training course. If a worker 
participates in multiple courses, each instance of his / her participation is counted separately. This 
means  that  the  participant  count  includes  multiple  entries  for  the  same  worker  if  they  engage  in 
multiple  courses  throughout  the  specified  period  (e.g.  a  year).  Therefore,  the  participant  count 
reflects  the  total  number  of  participations  by  workers,  including  multiple  instances  by  the  same 
individuals. 

Employed 

Number of employees of a company who can have participated in one or more CVTs. According to 
this definition, if the same worker participates in multiple courses within a specified period, they are 
counted  as  one  participant  rather  than  being  counted  multiple  times.  This  approach  ensures  that 
each employee is only counted once, regardless of the number of CVT courses they attend during 
the designated period. 

Both databases cover the years 2005, 2010, 2015, and 2020, and the enterprises are categorised according 
to their size:  

•  From 10 to 49 persons employed;  

•  From 50 to 249 persons employed;  

• 

250 persons employed or more.  

However,  the  data  lack  specific  information  regarding  the  types  of  courses  and  trainings  included. 
Consequently, it is challenging to determine if all the training activities recorded in the data align with 
the activities covered by the desired result indicator. Access to microdata collected through the survey, 
which could be made available upon request, has the potential to address this limitation by providing 
more comprehensive details. In fact, the survey asked enterprises to identify the three most significant 
skills covered by the funded courses, including general IT skills, IT professional skills, management 
skills, team working skills, customer handling skills, problem-solving skills, office administration skills, 
foreign language skills, technical or job-specific skills, oral or written communication skills, numeracy 
or literacy skills, and other skills. If the data are available a selection can be made by focusing on IT and 
management  skills  to  identify  the  training  programmes  that  contributed  the  most  to  smart 
specialisation, industrial transition, and entrepreneurship.  

It is worth mentioning that the databases offer information on various aspects related to the cost of CVT 
courses, including total course cost, direct costs, labour costs of participants, and net contributions to 
training funds.  

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Finally,  both  databases  have  two  complementary  databases  available  for  consultation,  which  can 
provide  additional  details50.  The  data  cover  the  years  2010,  2015,  and  2020,  and  the  enterprises 
providing  CVT  were  classified  using  aggregation  of  NACE  codes.  In  particular  the  enterprises  are 
classified according to the following categories:  

• 

Industry, excluding construction (NACE sections: B-E); 

•  Construction (NACE section F); 

•  Wholesale  and  retail  trade,  transport,  accommodation,  and  food  service  activities  (NACE 

sections: G-I); 

• 

Information and communication; financial and insurance activities (NACE sections  J and K); 

•  Real  estate  activities;  professional,  scientific  and  technical  activities;  administrative  and 
support  service  activities;  arts,  entertainment  and  recreation;  other  service  activities  (NACE 
sections  L-N, R and S). 

However, the two databases do not provide classifications based on the size of the enterprises, which 
means  that  it  is  not  feasible  to  determine  the  specific  costs  associated  with  SMEs.  Instead,  these 
databases can be used as an approximation or proxy to gain insights into the variations in training costs 
across different industries. This enables a more comprehensive analysis at the sector level, providing a 
broader understanding of the trends and patterns related to training expenses. 

Table 28. Overview of potential data sources to establish financial amounts in the area of skills for 
smart specialisation and transition 

POTENTIAL 

DATA 

SOURCE(S) 

RELATED 

INDICATOR(S) 

ADVANTAGES 

LIMITATIONS 

Cost of CVT 
courses by type 

SMEs staff completing 
training for skills for 

Provide the average cost of an  
SME employee attending a 

No microenterprises 

included in the data; 

and size class -
cost per 

smart specialisation, for 
industrial transition and 

participant 

entrepreneurship 

training; 

Provides estimates for all EU 

Do not allow access to info 
on the typology of training 

countries; 

funded; 

(RCR98). 

Possible to have access to 

Data are based on a survey 

microdata for further details; 

Detailed information on the 

costs are provided (e.g. direct 

cost, labour cost…). 

carried out on a five-year 
basis and therefore not 

promptly updated. 

50 Database TRNG_CVT_19N2, 
https://ec.europa.eu/eurostat/databrowser/view/TRNG_CVT_19N2/default/table?lang=en&category=educ.educ_part.trng_cvt.trng_cvt_03v  
Database TRNG_CVT_18N2, 

https://ec.europa.eu/eurostat/databrowser/view/TRNG_CVT_18N2/default/table?lang=en&category=educ.educ_part.trng_cvt.trng_cvt_03  

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POTENTIAL 
DATA 

SOURCE(S) 

RELATED 

INDICATOR(S) 

ADVANTAGES 

LIMITATIONS 

Cost of CVT 

n. of employees being 

Provide the average annual 

No microenterprises 

courses by type 

and size class -–

cost per person 

employed in 

enterprises 

providing CVT 

courses 

trained over a year. 

cost for an SME to train an 

included in the data; 

employee; 

Do not allow access to info 

Provides estimates for all EU 

on the typology of training 

countries; 

funded; 

Possible to have access to 

microdata for further details; 

Data are based on a survey 
carried out on a five-year 

Detailed information on the 
costs is provided (e.g. direct 

cost, labour cost…). 

basis and therefore not 

punctually updated; 

It adopts a different unit of 
measurement than the 

common indicator RCR98. 

2.4.2.4 

Feasibility assessment 

As established in earlier sections, the  potential to develop an FNLC  solution(s)  was assessed  in  one 
intervention  field  related  to  sustainable  smart  specialisation  sub-area:  developing  skills  for  smart 
specialisation,  industrial  transition  and  entrepreneurship.  A  total  of  14  Member  States  support 
operations in the field, covering 98 % of programmes in the examined area.  

Based on a preliminary analysis of the most frequently adopted indicators by the OPs under the sub-
area  of  skills  for  smart  specialisation  and  transition,  along  with  the  results  of  the  desk  research  to 
identify potential data sources, two main options were identified. 

The first option relies on the common result indicator ‘RCR98 – SMEs staff completing training for 
skills  for  smart  specialisation,  for  industrial  transition  and  entrepreneurship’  focusing  on  the 
expenses associated with an employee's participation to a training activity. 

The second option relies instead on data provided by the database Cost of CVT courses by type and 
size class cost per person employed in all enterprises . Specifically, this option examines the average 
cost incurred by an SME to train an employee in one year. Based on these data, a scheme reimbursing 
on  the  basis  of  each  employee  trained  in  the  given  period  of  time  could  be  potentially  built.  In  this 
approach,  the  average  cost  incurred  by  an  SME  to  train  an  employee  within  a  one-year  period  is 
examined. By analysing these data, it becomes possible to establish a scheme that offers reimbursement 
based on the number of employees trained during a specific timeframe.  

These  approaches  allow  for  a  potential  incentivisation  mechanism,  where  SMEs  are  encouraged  to 
invest  in  training  their  workforce  by  offering  them  financial  support  proportional  to  the  number  of 
employees they train. 

The  table  below  summarises  the  potential  FNLC  solutions  identified  by  the  research  team  for  the 
development of skills for smart specialisation, industrial transition. For each solution, we provide the 
results  to  be  achieved,  indicator  name,  measurement  unit,  verification  mechanism,  key  risks  and 
potential data sources to establish financial amounts.  

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Table  29.  Summary  of  potential  FNLC  solutions in  the  area  of  skills  for  smart  specialisation  and 
transition 

COMPONENT 

Potential data sources 

OPTION 1 

OPTION 2 

Developing skills for smart specialisation, industrial transition 
and entrepreneurship 

EUROSTAT  database  ‘Cost  of 
CVT  courses  by  type  and  size 
class – cost per participant’. 

EUROSTAT  database  'Cost  of 
CVT  courses  by  type  and  size 
cost  per  person 
class 
employed 
enterprises 
providing CVT courses’. 

in 

– 

Potential FNLC approach  

Target-based  approach  based 
on  the  number  of  participants 
completing training. 

Target-based  approach  based 
on  the  number  of  employees 
or  more 
one 
completing 
trainings in one year. 

Description of the operation 
type  

Description of results to be 
achieved with a timeline  

Indicator name  

targeting 

the  development  of  skills 

Operation 
for  smart 
specialisation, industrial transition, and entrepreneurship through 
the provision of training. It focuses on creating a skilled workforce 
that can seize opportunities from the green and digital transitions 
within businesses. 

The release of funds is based on 
numbers  of  participants 
in 
training  courses  (for  skills  for 
smart 
for 
industrial 
and 
entrepreneurship).  

specialisation, 
transition 

employees 

The release of funds is based on 
numbers  of 
in 
training  courses  (for  skills  for 
for 
smart 
industrial 
and 
entrepreneurship). 

specialisation, 
transition 

N. of employees trained. 

for 

SMEs  staff  completing  training 
smart 
for 
skills 
specialisation, 
industrial 
transition and entrepreneurship 
(RCR98). 

for 

Measurement unit  

The  measurement  unit 
‘participant’.  

is 

The  measurement  unit 
‘employee’. 

is 

 Verification mechanism  

Documentation 
providing 
evidence  of  the  number  of 
participants,  such  as  proof  of 
course attendance. 

Documentation 
evidence  of 
employees 
proof of course attendance. 

providing 
the  number  of 
trained,  such  as 

providing 
Documentation 
evidence  of  the  employment 
status of the participants. 

providing 
Documentation 
evidence  of  the  employment 
status of the participants. 

Key risks and measures to 
prevent them  

Beneficiaries  may  choose  to 
offer  training  courses  of  low 

In  the  case  under  analysis,  the 
main  concern  is  the  potential 

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increasing 

quality  or  courses  that  are  not 
entirely relevant to the scope of 
the  intervention  field,  with  the 
the 
intention  of 
number of participations rather 
than 
the 
the  quality  of 
training. To  mitigate 
such 
unintended  consequences,  it  is 
request 
beneficial 
to 
information  on 
topics 
covered  in  the  training.  This 
helps in assessing the relevance 
and  alignment  of  the  training 
with  the  intervention  field.  By 
obtaining information about the 
training 
it  becomes 
possible  to  evaluate  whether 
the 
the  courses  align  with 
intended  objectives  and  are  in 
accordance  with  the  desired 
outcomes of the intervention. 

topics, 

the 

than 

offer 

fewer 

perverse incentive of providing 
a  smaller  number  of  trainings 
than  what  could  be  provided 
with the sum received from the 
programme. This incentive may 
arise when organisations choose 
training 
to 
their 
opportunities 
allocated 
allows, 
budget 
potentially  limiting  the  reach 
and  impact  of  the  programme. 
To  mitigate  such  unintended 
consequences, it is beneficial to 
the 
request 
of 
number 
trainings. 
in 
assessing 
the  relevance  and 
alignment  of  the  training  with 
the 
field.  By 
obtaining information about the 
training 
it  becomes 
possible to evaluate whether the 
courses align with the intended 
objectives and are in accordance 
with  the  desired  outcomes  of 
the intervention. 

information  on 
and  duration 
helps 

intervention 

topics, 

This 

2.4.3.  FC3: Climate change adaptation and risk prevention 

In line with Article 5(1) of Regulation (EU) 2021/1060, the ERDF/CF will provide support for various 
objectives, including the aim to contribute to ‘a greener, low-carbon transitioning towards a net zero 
carbon economy and resilient Europe by promoting clean and fair energy transition, green and blue 
investment,  the  circular  economy,  climate  change  mitigation  and  adaptation,  risk  prevention  and 
management, and sustainable urban mobility’ (Policy Objective 2). 

This  feasibility  assessment  focuses  on  one  specific  objective  within  Policy  Objective  2,  which  is 
promoting climate change adaptation, disaster risk prevention and resilience, taking into account 
ecosystem-based approaches (RSO2.4).  

The  section  is  organised  as  follows.  Firstly,  we  present  the  primary  findings,  providing  detailed 
information  on  the  operations  under  consideration  for  the  result-based  tools,  along  with  potential 
indicators and data sources that could be utilised to determine the funding associated with achieving 
the desired results. Secondly, we provide an overview of the potential alternatives for the selected area, 
highlighting the most promising options. 

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2.4.3.1  Operations considered for result-based tools 

The initial stage of the analysis carried out by the research team involved reviewing, classifying, and 
defining operations that were considered for result-based tools. To accomplish this, we examined the 
available information on 119 programmes scheduled for the period 2021-2027, spanning 21 Member 
States, funded by either the ERDF or the CF (according to the Dataset ‘2021-2027 Finances details’ of 
the  European  Commission  Cohesion  Open  Data  Platform)51.  Having  conducted  this  review,  it  was 
observed that programmes falling under the sub-area of climate change adaptation and risk prevention 
were classified into 22 different intervention fields. Table 30 shows a list of the most common ones.

51 Available at: https://cohesiondata.ec.europa.eu/2021-2027-Categorisation/2021-2027-Finances-details-categorisation-multi-fu/hgyj-gyin. Last 
access: 6 Apr 2023. 

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Table 30. Intervention fields in the area of climate change adaptation and risk prevention 

Adaptation to 
climate change 
measures and 
prevention and 
management of 
climate-related 
risks: floods and 
landslides 

Adaptation to 
climate change 
measures and 
prevention and 
management of 
climate-related 
risks: fires 

Adaptation to 
climate change 
measures and 
prevention and 
management of 
climate-related 
risks: others, e.g. 
storms and 
drought 

Risk prevention 
and management 
of non-climate-
related natural 
risks and risks 
linked to human 
activities 

Water 
management and 
water resource 
conservation 

Nature and 
biodiversity 
protection, natural 
heritage and 
resources, green 
and blue 
infrastructure 

Support to entities 
that provide services 
contributing to the 
low-carbon economy 
and to resilience to 
climate change, 
including 
awareness-raising 
measures 

Other measures to 
reduce greenhouse gas 
emissions in the area 
of preservation and 
restoration of natural 
areas with high 
potential for carbon 
absorption and storage 

BE 
BG 
CY 
CZ 
DE 
EE 
EL 
ES 
FI 
FR 
HR 
HU 
IT 
LT 
LV 
PL 
PT 
RO 
SE 
SI 
SK 
Total 

X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
20 

X 
X 
X 
X 
X 
X 
X 

X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
19 

X 

X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
19 

X 

X 

X 
X 
X 

X 

X 
X 

8 

X 

X 
X 

X 
X 
X 
X 
X 

X 
X 
X 
X 

X 
13 

Source: Prepared by the study team, based on the dataset ‘2021-2027 Finances details’. 

Note: Only fields covered by programmes in three or more Member States are included in the table.

X 

X 
X 
X 
X 

X 

6 

X 
X 
X 

X 
X 

5 

X 

X 

X 

3 

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In  Box  5,  we  illustrate  a  potential  intervention  in  climate  change  adaptation  and  risk  prevention  by 
presenting an example of a regional programme in Spain that covers five different intervention fields.  

Box 5. Programme Galicia ERDF 2021-2027 (Spain) 

The ERDF funds support investments in priority areas through regional and interregional programmes, which 

include investments in various areas, such as business competitiveness, sustainability, connectivity and social 
inclusion. Among these areas, one topic is the funding of initiatives aimed at adapting to climate change and 

implementing measures to enhance preparedness and response to manage high-priority risks such as floods, 
droughts, and fires. Below are some examples of operations covered within this domain in Programme Galicia: 

•  Actions to preserve water resources, their ecosystems and preparation, protection and response to floods, 
such as actions against the deterioration of the riverbanks and their ecosystems, actions to prepare, protect 
and  respond  to  floods,  protection  and  prevention  of  deterioration  of  wetlands  and  flood  plains  and 
restoration of fluvial continuity; 

•  Actions  that  contribute  to  mitigating  this  risk  of  more  intense  rainfall  through  the  execution  of  works 

• 

guaranteeing the safety of slopes and walls that prevent landslides; 
Preventive actions to reduce the risk derived from extreme weather events associated with climate change, 
reinforcing  the  preparation  and  response  actions  necessary  to  address  the  management  of  these  risks 
comprehensively,  such  as  providing  the  operational  and  technological  equipment,  facilities  and 
infrastructures destined for the anticipation, prevention, planning and immediate response to natural risks, 
climatic phenomena and derived from human action; 

•  Measures aimed at increasing resilience to climate change, such as improving the early warning tools that 
currently exist for risks derived from climate change (e.g. climate observation systems and their effects, 
improvements in numerical models and data processing systems), development of sectoral risk assessment 
tools, collaboration with the local administration in the development and implementation of the adaptation 
measures,  encouraging  the  dissemination  of  knowledge  by  promoting  synergies  of  actions  as  well  as 
promoting the training and awareness of Galician society in the face of the challenges of climate change 
through training and other types of initiatives; 

•  Acquisition of different integrated vehicles equipped with the necessary elements to carry out prevention 
actions against forest fires, as well as their employment as support vehicles for defence/extinction efforts. 

Intervention fields covered by projects within the programme:  

1.  Adaptation to climate change measures and prevention and management of climate related risks: fires; 
2.  Adaptation  to  climate  change  measures  and  prevention  and  management  of  climate  related  risks: 

floods and landslides; 

3.  Adaptation  to  climate  change  measures  and  prevention  and  management  of  climate  related  risks: 

others, e.g. storms and drought; 

4.  Risk  prevention  and  management  of  non  climate  related  natural  risks  and  risks  linked  to  human 

activities; 

5.  Support to entities that provide services contributing to the low carbon economy and to resilience to 

climate change, including awareness‑raising measures. 

Source: Dataset ‘2021-2027 Finances details’, complemented by official programme documentation52.

52 Available at: https://www.fondoseuropeos.hacienda.gob.es/sitios/dgfc/es-
ES/ipr/fcp2020/P2127/PF/Documents/Programa_de_Galicia_FEDER_2021-2027.pdf and https://ec.europa.eu/regional_policy/in-your-
country/programmes/2021-2027/es/2021es16rfpr014_en. Last access: 8 May 2023. 

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Having reviewed the compilation of intervention fields found in the list of 119 ongoing programmes 
mentioned above  funded by the ERDF/CF, it becomes clear that projects in this domain can have a 
wide-ranging scope, encompassing operations across multiple fields of intervention simultaneously (as 
can be observed in the Spanish example). However, despite the diversity of intervention fields within 
the selected sub-area, a notable degree of overlap is also observed among the supported operations in 
the analysed programmes. 

For  example, when examining the  overlap of the  top  intervention fields within the  realm of climate 
change adaptation and risk prevention (Figure 5), it becomes evident that the most prevalent one is the 
prevention and management of floods and landslides, featured in 90 % of the programmes. Following 
closely, the second most prominent field is the prevention and management of other climate-related 
risks, such as storms and  droughts, encompassing 71 % of the  programmes.  Ranking third, projects 
associated  with  the  prevention  or  management  of  fires  are  included  in  55 %  of  the  programmes. 
Finally,  the  prevention  or  management  of  non-climate  and  human-induced  risks  is  present  in 
approximately one third of the programmes. 

Figure  5.  Overlap  of  intervention  fields  in  the  sub-area  of  climate  change  adaptation  and  risk 
prevention (Venn diagram) 

Source: Prepared by the study team, based on the dataset ‘2021-2027 Finances details’. 

Considering the weight of these four intervention fields, the next sections of the present feasibility check 
will  focus  on  potential  FNLC(s)  for  investment  in  the  prevention  or  management  of  floods  and 
landslides, fires, other climate-related risks and non-climate and human-induced risk. 

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2.4.3.2  Result indicators considered for result-based tools 

In this section, we provide a detailed explanation of the potential indicators that have been taken into 
account for the development of FNLC solutions. To illustrate the intervention logic for climate change 
adaptation and risk prevention, Figure 6 presents the outcomes and results of funded operations within 
the specific area of investment under examination. The focus is primarily on operations relating to the 
prevention or management of floods, landslides, fires, and other climate-related risks, as well as non-
climate  and  human-induced  risks.  The  outputs,  results,  and  impacts  depicted  in  the  figure  draw 
inspiration from Regulation (EU) 2021/1058 of the European Parliament and Council, which establishes 
common indicators for assessing each specific objective for the ERDF and the CF53. 

Figure 6. Climate change adaptation and risk prevention intervention logic 

Source: Prepared by the study team based on Regulation (EU) 2021/1058. 

The  common  indicators  utilised  by  ERDF  and  the  CF  serve  as  valuable  resources  when  selecting 
indicators  for  developing  FNLC  solutions.  These  indicators  are  particularly  useful  because  they  are 
aligned  with  the  core  strategies  of  the  European  Union,  enabling  comparisons  across  various 
programmes  and  Member  States.  By  relying  on  these  indicators,  a  consistent  and  standardised 
approach can be taken. 

The  primary  source  of  up-to-date  information  regarding  the  common  ERDF/CF  result  indicators  is 
national agents, such as programme Managing Authorities. However, an initial glance at the indicators 
being tracked in the current period can be obtained from the dataset ‘2021-2027 Achievement Details’, 
available  on  the  European  Commission  Cohesion  Open  Data  Platform54.  This  dataset  contains 
information on the indicators used to measure the performance of adopted programmes for the period 
2021-2027, such as common and specific output and result measures. Currently, the dataset provides 
information on the planned indicator values. However, starting from 2023, it is planned to incorporate 

53 Available at: https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:02021R1058-20210630&qid=1681300139712&from=en. Last 
access: 13 Apr 2023. 
54 Available at: https://cohesiondata.ec.europa.eu/2021-2027-Indicators/2021-2027-Achievement-Details-multi-funds-/xi3a-zddk. Last access: 12 
Apr 2023. 

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time-series  information  on  targets,  as  well  as  cumulative  amounts  reported  for  decided  and 
implemented values. 

Based  on  the  above-mentioned  dataset,  we  identified  the  following  result-based  indicators  being 
tracked during the 2021-2027 period by projects funded by ERDF/CF in climate change adaptation and 
risk prevention: 

•  Estimated greenhouse emissions (RCR29); 
• 
Jobs created in supported entities (RCR01); 
•  Population benefiting from flood protection measures (RCR35); 
•  Population  benefiting  from  protection  measures  against  climate-related  natural  disaster 

(other than flood and wildfires) (RCR37); 

•  Population benefiting from protection  measures against non-climate-related natural risks 

and risks related to human activities (RCR96); 

•  Population benefiting from wildfire protection measures (RCR36); 
•  Population having access to new or improved green infrastructure (RCR95); 
•  Users of new and upgraded public digital services, products and processes (RCR11); 
•  Visitors of cultural and tourism sites supported (RCR77); 

Table 31 shows the number of Member States tracking each indicator mentioned above. It can be seen 
that the most used common indicators in the current period for programmes related to climate change 
adaptation and risk prevention are: population benefiting from flood protection measures (RCR35), 
which is being tracked by 20 Member States; population benefiting from protection measures against 
climate-related  natural  disaster  (other  than  flood  and  wildfires)  (RCR37),  which  is  tracked  by  16 
Member  States;  population  benefiting  from  wildfire  protection  measures  (RCR36),  tracked  by  14 
Member  States;  and  population  benefiting  from  protection  measures  against  non-climate-related 
natural  risks  and  risks  related  to  human  activities  (RCR96),  tracked  by  9  Member  States.  Table  32 
shows an overview of these indicators. 

Table 31. Common ERDF/CF indicators in the area of climate change adaptation and risk prevention 

RCR35  RCR37  RCR36  RCR96  RCR95  RCR01  RCR11  RCR29  RCR77 

BE 

BG 

CY 

CZ 

DE 

EE 

EL 

ES 

FI 

FR 

HR 

HU 

IT 

LT 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

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RCR35  RCR37  RCR36  RCR96  RCR95  RCR01  RCR11  RCR29  RCR77 

X 

X 

X 

X 

X 

X 

LV 

PL 

PT 

RO 

SE 

SI 

SK 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

TOTAL 

20 

16 

14 

9 

4 

2 

2 

1 

1 

Source: Prepared by the study team, using the dataset "2021-2027 Achievement Details". 

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Table 32. Overview of most common indicators in the area of climate change adaptation and risk prevention 

CODE 

INDICATOR NAME 

MEASURE

DEFINITION AND CONCEPTS 

MENT 

UNIT 

TIME 

MEASUREMENT 

ACHIEVED 

RCR35 

Population benefiting from flood 

persons 

Population  living  in  areas  where  protection  infrastructure  (including  also  green  infrastructure  for 

Upon completion 

protection measures 

adaptation  to  climate  change)  is built or  significantly  upgraded  in  order  to reduce vulnerability  to 

of output in the 

flood risks. The indicator counts the resident population at risk of flooding. 

supported project 

RCR36 

Population benefiting from wildfire 

persons 

Population living in areas exposed to wildfire risks and where vulnerability to wildfires decreases as 

Upon completion 

protection measures 

a result of the supported projects. The indicator covers protection measures which are clearly localised 

of output in the 

in high-risk areas and which directly address wildfires risks, as opposed to more general measures 

supported project 

implemented at national or regional level. 

RCR37 

Population benefiting from protection 

persons 

Population living in  areas  exposed  to climate-related natural risks,  other  than  floods and  wildfires 

Upon completion 

measures against climate-related 

(storms,  droughts,  heatwaves),  and  where  vulnerability  to  such  risks  decreases  as  a  result  of  the 

of output in the 

natural disaster (other than flood and 

supported projects. The indicator covers protection measures, areas at risk and which directly address 

supported project 

wildfires) 

the specific risks, as opposed to more general measures implemented at national or regional level. 

RCR96 

Population benefiting from protection 

persons 

Population  living in  areas  exposed  to  non-climate-related  natural risks  and risks  related  to  human 

Upon completion 

measures against non-climate-related 

activities, and where vulnerability to such risks decreases as a result of the supported projects. The 

of output in the 

natural risks and risks related to 

indicator covers protection measures which are clearly localised in high-risk areas and which directly 

supported project 

human activities 

address the specific risks, as opposed to more general measures implemented at national or regional 

level. 

Source: Metadata of common ERDF/CF/JTF indicators55.

55 Available at: https://cohesiondata.ec.europa.eu/2021-2027-Indicators/2021-2027-ERDF-CF-JTF-Common-Indicators/4t73-mihb. Last access: 13 Apr 2023. 

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After conducting desk research, we evaluated each of the chosen result-based indicators according to 
the criteria outlined in Table 33. Our assessment of these indicators can be found in Table 34. 

Table 33. Criteria for feasibility assessment 

CRITERIA 

DESCRIPTION 

Relevance  

Relevance  of  the  results  indicator  towards  main  EU  strategies,  based  on  desk 
research and/or consultation with Member States.  

Robustness 

Relates  to  the  available  data,  its  completeness  and  comprehensiveness.  This 
presumes  that  all  data  points  needed  for  calculations  and  establishment  of  a 
particular  FNLC  are  sufficiently  covered  in  the  data  sample,  whereas  the  latter 
provides a reasonable coverage of Member States. Incomplete data (i.e. provisional 
/  estimated data or data gaps) are as dangerous as inaccurate data. Gaps in data 
lead to a partial view of the overall picture. Without a complete picture, FNLC may 
be calculated through uninformed actions. 

Practicality 

& 

perversity 

Relates to the administrative burden of the prospective arrangements for the audit 
trail  and  the  documentation  required  to  verify  that  results  have  been  achieved 
and/or conditions were met. Administrative burden level depends on the scope, 
detail  and  accessibility  of  evidence  required  to  validate  or  invalidate  the 
achievement of results. It also relates to the possibility of undesirable effects of the 
FNLC, risks of perverse incentives or unintended negative effects of applying the 
proposed  FNLC  solution  (such  as  creaming  /  cherry-picking  in  the  selection  of 
participants or parking). 

Source: Prepared by the study team. 

Table  34.  Assessment  of  selected  indicators  in  the  area  of  climate  change  adaptation  and  risk 
prevention 

CRITERIA 

DESCRIPTION 

Relevance 

These  common  ERDF/CF  indicators  are  overall  relevant,  as  they  are  rooted  in  the  main  EU 

strategies, being directly related to the area of climate change adaptation and risk prevention. 
More precisely: 

• 

• 

• 

population  benefiting  from  flood  protection  measures  is  related  to  the 

intervention field of prevention and management of floods and landslides; 
population  benefiting  from  wildfire  protection  measures  is  related  to  the 

intervention field of prevention or management of fires; 
population benefiting from protection measures against climate-related natural 

disaster  (other  than  flood  and  wildfires)  is  related  to  the  intervention  field  of 
prevention and management of other climate-related risks; 

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CRITERIA 

DESCRIPTION 

• 

population  benefiting  from  protection  measures  against  non-climate-related 
natural risks and risks related to human activities is related to the intervention field 
of prevention or management of non-climate and human-induced risks.  

Robustness 

Based on desk research and information available on programmes being implemented in the 
2021-27 period and on information on indicators in the area of climate change adaptation and 
risk prevention provided by 21 Member States the result-based indicators on: 

• 

• 

• 

• 

population benefiting from flood protection measures is tracked by 20 Member 
States in the current period, indicating high comprehensiveness of the indicator; 

population  benefiting  from  wildfire  protection  measures  is  tracked  by  14 
Member  States  in  the  current  period,  indicating  high  comprehensiveness  of  the 

indicator; 
population benefiting from protection measures against climate-related natural 

disaster (other than flood and wildfires)  is tracked by 16 Member States in the 
current period, indicating high comprehensiveness of the indicator; 

population  benefiting  from  protection  measures  against  non-climate-related 
natural risks and risks related to human activities is tracked by 9 Member States 
in the current period, indicating moderate comprehensiveness of the indicator. 

At  the  moment, the  study team  does  not have  access  to  the  collected  data  from  countries  in 
order  to  verify  its  completeness  /  quality.  The  information  will  be  fact-checked  after  further 
consultations with the Member States. 

Practicality & 
perversity 

An  FNLC  based  on  population  benefiting  from  protection  measures  should  not  be 
administratively burdensome, as data on population living in areas benefited by the projects 
should be easily accessible through statistical agencies and/or local authorities. 

Perverse incentives such as creaming and cherry-picking are reduced for the four indicators, 
but  still  present.  Beneficiaries  might,  for  instance,  choose  to  implement  projects  in  more 

populated areas to receive a larger outcome-based payment, therefore neglecting less populated 
areas that could potentially be more prone to disasters. There is also a risk that beneficiaries 

focus more on increasing the coverage of the project (i.e. increasing population covered) than 
the quality of the service provided. Additionally, the pressure to deliver results may also cause 

the beneficiaries to downplay certain risks or exclude them from the project scope, potentially 
leaving gaps in the effectiveness of the operations. To address these aforementioned perverse 

incentives,  conducting  a  thorough  evaluation  of  potential  risks  and  establishing  quality 
assurance mechanisms can be useful. These measures are, however, advisory and thus do not 

require  inclusion  in  the  audit trail within  a  potential  EU-Level  FNLC  scheme  but  could  be 
potentially implemented at the programme level in due course. 

Source: Prepared by the study team. 

2.4.3.3  Amounts linked to achievement of results 

Concerning  establishing  financial  amounts  for  the  selected  indicators,  the  study  team  revised  three 
potential data sources. 

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One possible way to assign financial values to the indicators presented earlier is by using a disaster 
damage database. Using these databases allows us to estimate the economic viability of investments 
made to reduce losses (Mazhin et al., 2021)56. One example of such data source is the Emergency Events 
Database (EM-DAT)57, a global source  of information on natural and technological disasters, which 
covers  over  20 000  disasters  worldwide  that  occurred  since  1900.  The  dataset  is  maintained  by  the 
Centre for Research on the Epidemiology of Disasters at the School of Public Health of the Université 
Catholique de Louvain in Belgium. It is considered the primary source of epidemiological information 
about  disasters,  combining,  in  a  uniform  format,  information  from  various  sources,  including  UN 
agencies,  non-governmental  organisations,  insurance  companies,  research  institutes,  and  press 
agencies (Mazhin et al., 2021). 

The EM-DAT can be useful in assigning financial values to the indicators discussed previously as it 
provides geographical, temporal, human and economic information on disasters at the country level. 
Among other variables, it records data on the date of the disaster, the number of deaths and people 
affected by the disaster, as well as the estimated economic impacts, e.g. the value of all damages and 
economic  losses  directly  or  indirectly  related  to  the  disaster  (in  US  dollars).  It  also  distinguishes 
between disaster groups (natural and technological) and disaster types, such as drought, earthquake, 
epidemic,  extreme  temperature,  flood,  glacial 
landslide, 
miscellaneous accident, storm, transport accident, volcanic activity and wildfire.  

industrial  accident, 

lake  outburst, 

The dataset is frequently referenced, as it is an active and well-known data source for the assessment 
of disaster management (Mazhin et al., 2021), being also the only disaster database with global coverage 
that is publicly accessible (Jones et al., 2022)58. One of its main advantages is that it is internally updated 
daily, with publicly accessible information updated every 3 months (after validation and cross-checking 
of the data).  

However, despite having information on recorded disasters in most EU countries, it has a significant 
limitation – data gaps –  thus requiring the use of statistical techniques to manage the missing data and 
avoid  biases  in  estimations  (Jones  et  al.,  2022).  Another  disadvantage  is  that  the  disaster  threshold 
imposed  by  the  dataset  ignores  the  potential  effects  of  high-frequency  and  low-intensity  types  of 
disasters (Mazhin et al., 2021). That is because in order to be recorded, disasters must meet at least one 
of the following criteria: 1) at least 10 fatalities; 2) at least 100 affected individuals; 3) a declaration of a 
state  of  emergency;  or  4)  a  request  for  international  assistance.  Additionally,  a  downside  of  using  a 
disaster  damage  database  to  determine  financial  values  based  on  avoidance  costs  is  the  random 
occurrence  of  disasters,  which  necessitates  the  selection  of  equivalent  events  for  the  purpose  of 
comparison between countries. Even considering its limitations, this dataset has the potential to be used 
as a source to establish financial amounts, especially if it is used in combination with other sources of 
data on natural disasters59. 

Another potential source to establish financial amounts identified by the study team is the indicator 
‘climate-related economic losses by type of event’ (cli_iad_loss)60, disseminated by the Eurostat using 

56 Mazhin, S. A., Farrokhi, M., Noroozi, M., Roudini, J., Hosseini, S. A., Motlagh, M. E., ... & Khankeh, H. (2021). Worldwide disaster loss and 
damage databases: A systematic review. Journal of education and health promotion, 10. 
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8552254/pdf/JEHP-10-329.pdf  
57 Available at: https://public.emdat.be. Last access: 3 Mai 2023. 

58 Jones, R. L., Guha-Sapir, D., & Tubeuf, S. (2022). Human and economic impacts of natural disasters: can we trust the global data?. Scientific data, 
9(1), 572. https://www.nature.com/articles/s41597-022-01667-x  
59 Alternative databases that could be considered as sources to complement the data from the EM-DAT are the Natural Catastrophe Services 
(NatCatSERVICE) and the SIGMA (Swiss Re). However, given that these databases are not publicly available, the study team did not have access 
to the data in order to evaluate its quality. 

60 Available at: https://ec.europa.eu/eurostat/databrowser/view/CLI_IAD_LOSS__custom_6201561/default/table?lang=en. Last access: 15 Mai 2023. 

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information  from  the  European  Environment  Agency  (EEA).  It  measures  the  economic  losses  from 
weather  and  climate-related  events,  such  as  meteorological  (storms),  hydrological  (floods,  mass 
movements) and climatological (heatwaves, cold waves, droughts, forest fires). Total losses are shown 
as  million  euros  and  in  euros  per  inhabitant,  while  losses  by  type  of  hazard  (i.e.  meteorological, 
hydrological, and climatological) are displayed as million euro for the EU-27. In addition to the annual 
figures, a time-series based on 30-year moving average is also presented. The indicator is based on data 
from CATDAT of RiskLayer, a proprietary historical catastrophe loss database, which is not publicly 
accessible and was provided to the EEA under an institutional agreement. 

An advantage of this dataset is that data are updated every year and adjusted to account for inflation 
(currently, values are presented in 2020 constant prices). In addition, the dataset is very comprehensive, 
with information on most EU countries. However, the drawback of using this dataset remains the same 
as  previously  mentioned  regarding  the  EM-DAT.  It  is  challenging  to  establish  a  direct  correlation 
between  the  interventions  carried  out  within  the  ERDF/CF  projects  and  the  seemingly  random 
occurrence of disasters. Additionally, the Eurostat dataset lacks comprehensive information on events 
that would facilitate the identification of comparable disasters and the ability to analyse events of the 
same type separately and by different countries. Consequently, while we consider this dataset useful 
as a supplementary source for weather and climate-related indicators, without having access to their 
source data (i.e. the CATDAT by RiskLayer) we cannot rely on it as the  only information source for 
determining financial amounts associated with the indicators outlined in the previous section. 

As an alternative to financial data retrieved from disaster databases, financial amounts could also be 
established by either extrapolating data from specific Member States to other countries or using their 
strategy  as  inspiration.  In  that  regard,  one  experience  from  Croatia  can  be  cited,  (see  Box  6.).  The 
experience from Croatia is selected as it is related to the most prevalent intervention field within the 
realm  of  climate  change  adaptation  and  risk  prevention  for  ERDF/CF  funds,  i.e.  prevention  and 
management of floods and landslides, featured in 90 % of the analysed programmes. It also relates to 
the  most  common  indicator  being  tracked  in  the  area  under  examination,  i.e.  population  benefiting 
from flood protection measures (RCR35), which is being tracked by 20 Member States. 

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Box 6. Croatia's National Recovery and Resilience Plan 

Croatia's National Recovery and Resilience Plan (NRRP) was submitted to the European Commission 
on May 2021 under the Recovery and Resilience Facility (RRF) funds. The plan, estimated to cost more 
than EUR 6000 million, has two main objectives: 1) reflect the need to ensure Croatia's socio-economic 
recovery  and  long-term  development  post-Covid-19;  2)  rebuild  the  areas  damaged  by  the  two 
earthquakes that happened in 2020. The plan's overall structure consists of five components and one 
initiative, each to be executed through a mix of 222 measures expected to be implemented through the 
successful completion of 166 milestones and 206 targets61,62.  

Within its investments, the plan covers a Disaster Risk Reduction Programme that aims to enhance 
measures for protecting against floods, specifically focusing on nature-based solutions. This involves 
revitalising watercourses, connecting abandoned sleeves and creating secondary wetland habitats, as 
well as removing invasive species. The programme comprises two main sub-measures. The first is a 
flood risk reduction programme, which concentrates on mitigating flood risks in the largest Croatian 
rivers of the Danube River basin. It involves constructing defensive embankments incorporating broad 
inundation areas along watercourses, aligning with nature-based solutions and the principle of ‘give 
space  to  rivers’.  The  second  component  focuses  on  revitalising  freshwater  systems,  including  the 
restoration and preservation of the Mirna River area, Vransko Lake, and Trakoscan Lake. Additionally, 
it aims to remove invasive species within the protected Neretva Delta region63.  

The Disaster Risk Reduction Programme has a budget of EUR 157.7 million and, among its targets, 
aims to have at least 20 000 residents benefiting from improved flood protection measures by 2026, 
i.e. a budget of EUR 7 885 per benefited resident64.  

Table 35 provides an overview of the potential data sources discussed in this section. 

61 Available at: https://www.europarl.europa.eu/RegData/etudes/BRIE/2022/733580/EPRS_BRI(2022)733580_EN.pdf. Last access: 15 May 2023. 

62 Available at: https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:52021SC0197&from=EN. Last access: 15 May 2023. 

63 Available at: https://commission.europa.eu/system/files/2021-07/com-2021-401_annexe_en.pdf.  Last access: 15 May 2023. 

64 The value of euros per benefited resident is calculated by dividing the total budget for the Disaster Risk Reduction Programme (€ 157.7 million 
by the selected target (20 000 benefited residents). However, it is important to note that, to receive the total amount in the Croatian case, other 
targets should also be met in addition to the number of residents benefiting from improved flood protection measures. They are: achieving at 
least 20 works contracts concluded for projects in the flood protection sector; at least 13 km of flood protective structures built in order to protect 
against the harmful effects of water; at least 2 km of restored watercourses including revitalisation of abandoned sleeves, permanent river and 
sleeve contact and investment in related infrastructure; at least 65 km of flood protective structures built in order to protect against the harmful 
effects of water; at least 16 km of restored watercourses including revitalisation of abandoned sleeves, permanent river and sleeve contact and 
investment in related infrastructure; and at least 77 km of flood protective structures built in order to protect against the harmful effects of water. 

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Table 35. Overview of potential data sources to establish financial amounts in the area of climate change adaptation and risk prevention 

POTENTIAL 
DATA SOURCE(S) 

Emergency Events 

Database (EM-
DAT) 

RELATED INDICATOR(S) 

ADVANTAGES 

LIMITATIONS 

Population benefiting from flood protection 
measures; 

Very frequent updates; 

Data gaps; 

Necessity to select comparable 

events in order to account for the 
seemingly random occurrence of 
disasters. 

Information at the 
country level; 

Uniform and comparable 
data; 

Publicly available. 

Population benefiting from wildfire protection 
measures; 

Population benefiting from protection 

measures against climate-related natural 
disaster (other than flood and wildfires); 

Population benefiting from protection 

measures against non-climate-related natural 
risks and risks related to human activities. 

METHODS TO ADJUST THE 
AMOUNTS 

For each disaster, the registered 

figure corresponds to the 
damage value at the moment of 

the event. The database, 
however, also presents values 

adjusted to current values in 
US dollars (in thousands) 

calculated using a consumer 
price index. These values 

should be converted to euro by 
applying the prevailing 
exchange rates. 

Climate-related 

economic losses by 
type of event 

Population benefiting from flood protection 
measures; 

(Eurostat, 
cli_iad_loss) 

Population benefiting from wildfire protection 
measures; 

Yearly updates; 

Disaggregation at the country level 

Data are updated every year 

Information at the 
country level. 

is only possible when looking at 
total economic losses caused by all 

and is adjusted to account for 
inflation (currently, values are 

types of disasters (meteorological + 
hydrological, + climatological); 

presented in 2020 constant 
prices). All monetary figures 
are presented in Euro. 

Population benefiting from protection 

measures against climate-related natural 
disaster (other than flood and wildfires); 

Lack of information that would 

allow selecting comparable events 
to account for the seemingly 
random occurrence of disasters. 

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POTENTIAL 
DATA SOURCE(S) 

Croatia's National 

Recovery and 
Resilience Plan 

RELATED INDICATOR(S) 

ADVANTAGES 

LIMITATIONS 

Population benefiting from flood protection 
measures. 

Financial values are 

Only one data point; 

calculated based on 
concrete experience with 
result-based financing. 

Necessity to extrapolate data for all 

Member States based on 
information from one country. 

METHODS TO ADJUST THE 
AMOUNTS 

After extrapolation of values to 

other  Member  States,  they  can 
be updated using relevant price 

index  figures  from  Eurostat 
(unit value for Member State X * 

index for Member State X), such 
as  the  Harmonised  Index  of 

Consumer 
(annual 
average  rate  of  change)  and/or 

Prices 

Labour 

the 
Index 
(percentage  change  compared 

Cost 

Source: Prepared by the study team. 

to previous year) in the relevant 
areas. 

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2.4.3.4 

Feasibility assessment 

As  established  in  earlier  sections,  the  potential  to  develop  an  FNLC  solution(s)  was  assessed  in  the 
intervention  fields  related  to  prevention  and  management  of  climate  and  non-climate-related  risks, 
such as floods and landslides, fires, storms and droughts, as well as human-induced risks. Together, 
these four intervention fields are present in all projects related to climate change adaptation and risk 
prevention, with the prevention and management of floods and landslides the most common, present 
in 90 % of the programmes evaluated by the study team. 

Table 36 summarises the potential FNLC solutions identified by the research team for the area under 
examination. For each solution, we also provide a description of operation types, results to be achieved, 
indicator  name,  measurement  unit,  verification  mechanism,  key  risks  and  potential  data  sources  to 
establish financial amounts. 

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Table 36. Summary of potential FNLC solutions in the area of climate change adaptation and risk prevention 

DESCRIPTION 

COMPONENT 

PREVENTION /  

MANAGEMENT OF 

FLOODS AND 

LANDSLIDES 

PREVENTION /  

MANAGEMENT OF 

FIRES 

PREVENTION /  

PREVENTION /  

MANAGEMENT OF 

MANAGEMENT OF NON-

OTHER CLIMATE-

CLIMATE AND HUMAN-

RELATED RISKS 

INDUCED RISKS 

Potential 

Target-based 

approach 

Target-based approach 

Target-based 

approach 

Target-based approach based 

FNLC 

approach 

based  on  the  number  of 

based  on  the  number 

based  on  the  number  of 

on  the  number  of  people 

people 

benefiting 

from 

of  people  benefiting 

people  benefiting 

from 

benefiting 

from  protection 

flood protection measures. 

from 

wildfire 

protection 

measures 

measures 

against 

non-

protection measures. 

against 

climate-related 

climate-related  natural  risks 

natural  disaster 

(other 

and  risks  related  to  human 

than flood and wildfires). 

activities. 

Description  of 

Operations  targeting  areas 

Operations 

targeting 

Operations 

targeting 

Operations 

targeting  areas 

the  operation 

where 

protection 

areas 

exposed 

to 

areas  exposed  to  climate-

exposed 

to 

non-climate-

type 

infrastructure 

(including 

wildfire 

risks 

and 

related 

natural 

risks, 

related natural risks and risks 

also green infrastructure for 

where  vulnerability  to 

other  than  floods  and 

related  to  human  activities, 

adaptation 

to 

climate 

wildfires decreases as a 

wildfires 

(storms, 

and  where  vulnerability  to 

change) 

is 

built 

or 

result of the supported 

droughts, 

heatwaves), 

such  risks  decreases  as  a 

significantly  upgraded 

in 

projects. 

and  where  vulnerability 

result  of 

the 

supported 

order 

to 

reduce 

vulnerability  to  flood  risks 

as a result of the supported 

projects. 

to such risks decreases as 

projects.  

a  result  of  the  supported 

projects. 

Description  of 

The release of funds is linked to achieving the following outcome: verified number of people benefiting from the 

results 

to  be 

specific protection measures (i.e. flood, wildfire, non-climate etc.) as a result of supported projects. As an example, 

achieved  with 

the release of funds could be contingent upon reaching a specific number of people within a designated timeframe, 

a timeline 

such  as  in  the  example  of  Croatia’s  National  Recovery  and  Resilience  Plan,  which  had  a  programme  focused  on 

measures for protecting against floods that aimed to have 20 000 residents benefiting from improved flood protection 

measures by 2026.  

It is worth noting that although certain milestones could be established, the study team currently does not have any 

specific  recommendations  regarding 

the most  optimum  milestones and timeframe that  would balance the 

simplification  and  financial  sustainability  aspects  of  the  proposed  FNLC  solution. Our  intention  is  to  engage  in 

discussions with Member States during the upcoming workshop in order to evaluate the possibility of developing 

more concrete and tangible proposals. 

Indicator name  Number 

of 

people 

Number  of  people 

Number 

of 

people 

Number of people benefiting 

benefiting 

from 

flood 

benefiting 

from 

benefiting 

from 

from  protection  measures 

protection  measures  as  a 

wildfire 

protection 

protection 

measures 

against  non-climate-related 

against 

climate-related 

natural risks and risks related 

natural  disaster 

(other 

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DESCRIPTION 

COMPONENT 

PREVENTION /  

MANAGEMENT OF 

FLOODS AND 

LANDSLIDES 

PREVENTION /  

MANAGEMENT OF 

FIRES 

PREVENTION /  

PREVENTION /  

MANAGEMENT OF 

MANAGEMENT OF NON-

OTHER CLIMATE-

CLIMATE AND HUMAN-

RELATED RISKS 

INDUCED RISKS 

result  of 

the  supported 

measures as a result of 

than  flood  and  wildfires) 

to human activities as a result 

projects. 

the supported projects. 

as 

a 

result  of 

the 

of the supported projects. 

supported projects. 

Measurement 

unit 

Number of persons 

Verification 

Document justifying how the target(s) was satisfactorily fulfilled, with appropriate links to the evidence, such as the 

mechanism 

list of protection measures undertaken and a documentary evidence that verifies the population for which the service 

is provided. 

Key  risks  and 

Perverse  incentives  such as  creaming and cherry-picking are reduced,  but  still  present as  beneficiaries  might,  for 

measures 

to 

instance,  choose  to  implement  projects  in  more  populated  areas  to  receive  a  larger  outcome-based  payment, 

prevent them 

neglecting less populated areas that could potentially be more prone to disasters. There is also a risk that beneficiaries 

focus  more  on  increasing  the  coverage  of  the  project  than  the  quality  of  the  service  provided.  Additionally,  the 

pressure to deliver results may also cause the beneficiary to downplay certain risks or exclude them from the project 

scope, potentially leaving gaps in the effectiveness of the operations. To address these perverse incentives, conducting 

a  thorough  evaluation  of  potential  risks  and  establishing  quality  assurance  mechanisms  can  be  useful.    These 

measures are, however, advisory and  thus  do  not  require inclusion  in  the audit trail  within a  potential EU-Level 

FNLC scheme but could be potentially implemented at the programme level in due course. 

Potential  data 

Emergency  events  databases,  such  as  the  EM-DAT,  can  be  useful  in  assigning  financial  values  to  the  indicators 

sources 

to 

discussed previously, as they provide economic information on disasters at the country level. Among other variables, 

establish 

financial 

amounts 

it records data on the value of all damages and economic losses directly or indirectly related to the disaster, which 

can be useful for an FNLC based on avoidance costs. Supplementary information can come from Eurostat through 

the indicator ‘climate-related economic losses by type of event’. To reduce the data gaps and triangulate  with the 

available  data,  it  would  be  beneficial  to  get  access  to  other  similar  databases,  such  as  the  CATDAT,  the 

NatCatSERVICE and the SIGMA. However, given that these last proprietary databases are not available for public 

consultation, the study team did not have access to the data in order to evaluate its quality. 

Alternatively, amounts could be assigned based on country examples, such as the case of Croatia's National Recovery 

and Resilience Plan, which had its budget released upon the completion of a set of predefined targets (among them 

achieving a specific number of people benefiting from flood protection measures). 

Source: prepared by the study team. 

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3.  Conclusions 

The operationalisation of methodology for EU-level SCOs and result-based tools was not a linear, but rather an 
iterative process. This means that the presented steps of the methodology were deeply intertwined with each 
other and helped facilitate the outputs from each respective part of the methodological process. As the study 
team has anticipated from the beginning, the process of developing EU-level SCOs and result-based tools was 
also  mainly  a  data-driven  exercise.  Because  of  this,  the  methodological  approach  had  to  provide  ample 
flexibility to extract the highest possible quality of data from a large sample of Member States. To achieve such 
results, the implementation of the proposed methodology had to be interrupted at times to reiterate and take 
corrective actions in our approach, such as to consider potential merging of different areas of the study in order 
to acquire a larger sample of data to be used for SCO calculations. Certain design elements of EU-level SCOs 
have  also  been  subject  to  corrective  actions  because  of  the  ongoing  feedback  loops  from  DG  REGIO, 
Transnational  Network  of  ERDF/CF  SCO  practitioners,  MAs  and  IBs  from  Member  States  and  other 
Commission stakeholders such as DG ENER. Overall, these were pivotal when adjusting the methodological 
process  accordingly  and  accommodating  any  underlying  issues  which  might  stem  from  the  data  collection 
process or the design of the proposed SCOs.  

For the development of EU-level SCOs, the study team prioritised the development of SCOs which reimburse 
Member States based on delivery of outputs and direct results (e.g. decrease in CO2 emissions in SMEs or public 
and  housing  sector),  as  opposed  to  input-based  SCOs  (e.g.  hourly  costs  of  direct  research  staff).  This  was  a 
conscious  decision  made  to  increase  the  simplification  effect  of  the  developed  SCOs,  as  documents  such  as 
timesheets  which  are  a  necessity  under  input-based  SCOs  are  not  required  under  arrangements  which 
reimburse an output or result indicator. 

In Area 1, the EU-level SCOs proposed by the study come in the form of a unit cost for decreasing annual GHG 
emissions by one ton of CO2 for both sub-areas. According to historical data, such an indicator was used across 
both analysed sub-areas more often than any other indicator of intervention outputs or results tracked by the 
Member States. Its versatility and suitability for all three types of intervention made it a logical choice for an 
EU-level SCO in Area 1. The proposed SCO offers a single unit cost rate per Member State. Member States still 
retain flexibility in choosing the activities they intend to carry out in their projects. However, the expenditure 
for these projects will be reimbursed through the single SCO rate, regardless of the type or number of eligible 
activities implemented in the intervention. The audit trail for this SCO would be limited to verifying the results 
achieved by the Member State (via an Energy Performance Certificate). The solution avoids potential perverse 
incentives such as overcompensation, as Member States will intrinsically achieve lesser results if  they opt to 
implement fewer eligible activities. The implementation of a single SCO rate will also reduce the administrative 
burden that would be associated with having to select different modules for each project and also avoid the 
inconsistencies  of  being  reimbursed  different  amounts  depending  on  the  types  of  activities  selected  for  the 
intervention. According to consulted stakeholders, interventions implemented across the Member States are 
heterogenous, which means that streamlining/standardisation attempts would reduce the attractiveness of the 
EU-level SCO. Therefore, the flexibility offered by the suggested approach is an appropriate representation of 
the needs of Member States.  

As regards sub-area 2A, the selection of options was heavily influenced both by the availability of sufficient 
quality data and the possibility to establish tangible definitions for SCOs in order for them to be achievable. 
The EU-level SCOs proposed in this sub-area is a unit cost for one month of gross new working position (that 
did not exist before) to directly perform R&D activities in the SME. From the policy perspective, the proposed 

129 

 
Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

option should help fulfil objectives set out by ‘Smarter Europe’ such as increasing partnership between private 
/ public research institutions and SMEs. Another added value proposition of this SCO is that it utilises a direct 
result  indicator  i.e.  job  creation  as  the  result  of  knowledge  exchange  projects  between  SMEs  and  research 
institutions. The SCO also utilises a common ERDF/CF output indicator – this should enable easier monitoring 
of achieved outcomes and thus allow for the SCO to be more widely used.  

In sub-area 2B, the proposed EU-level SCO considers a lump sum to cover the cost of one innovation voucher 
issued  to  an  SME  for  completing  innovation  /  digitisation  activities.  The  collected  sample  of  projects  in 
innovative start-up projects in environmental technology or digital products was very small. Therefore, we also 
took into account data on vouchers for creating and improving  any kind of innovative products in supported 
projects.  In the  case  of  innovation  vouchers,  however,  there  are  underlying  differences  in  how  the  Member 
States perceive and approach actual R&I activities. Accordingly, the cost of similar services (in terms of activities 
carried out) may differ significantly across the Member States. To that end, an outlier analysis was carried out 
to  avoid  setting  rates  which  do  not  comply  with  the  socioeconomic  logic  in  terms  of  the  overall  economic 
development and price level differences. A standardised lump sum should be a significant boost to promoting 
further SME growth in the area of innovation. Furthermore, a lump sum offers maximum simplification in that 
all costs of the operation are covered. The proposed SCO option also leaves the possibility for private co-funding 
by the SME.   

For  sub-area  2C,  the  study  team  proposed  not  developing  an  SCO  based  on either  historical  data  and/or 
alternative  sources.  While  most  Member  States  have  provided  historical  data  for  this  sub-area,  the 
data collected were very heterogeneous in terms of supported activities, cost categories and outputs. As such, 
the data were not comparable across the Member States. An EU-level SCO merged with other sub-areas was 
considered. The study team explored the possibility of utilising the collected historical data by merging it with 
historical data from sub-areas 2A and 2B. Upon reviewing the available data, the study team discovered that 
merging  data  with  other  sub-areas  was  largely  not  feasible.  The  data  provided  in  2C  covered  a  very  large 
number of activities and outputs but did not have many overlaps with other sub-areas. However, this yielded 
limited  results.  In  addition  to  that,  merging  such  data  on  a  larger  scale  would  have  increased  the  risk  of 
inaccurate  values since Member States implemented different activities and incurred different costs in these 
sub-areas.  

In sub-area 3A, the proposed EU-Level SCOs considers a lump sum to cover the cost of one SME attending a 
single international event (such as a trade fair, international conference, or partnership exchange). Although 
limited possibilities were foreseen by the study team to develop alternative calculation options, efforts were 
made to develop separate lump sums for EU and extra-EU events. However, due to the restricted availability 
of  event-specific  information  on  the  location  and  the  results  of  the  statistical  analysis,  this  option  had  to  be 
excluded.  This  standardised  lump  sum  has  the  potential  to  significantly  increase  SME  participation  in  fairs 
since it offers maximum simplification, covering all costs of the operation and allowing for an easy audit trail 
to be adopted. 

For sub-area 3B, the study team proposed a lump sum to cover the costs of an SME seeking consultancy or 
advisory services to develop an internationalisation strategy. Since this refers to projects characterised by a 
limited budget, the proposed EU-level SCOs have the potential to significantly reduce administrative burden 
for both SMEs and MA, thereby facilitating and encouraging the implementation of the operation. It is worth 
highlighting that the minimal requirements provided in the audit trail allow greater flexibility and optimise the 
adoption of the scheme. To ensure the achievement of high-quality results, it is important to carefully select 
valuable  projects  and  provide  detailed  information  in  the  call  for  proposals  about  the  expected  results.  In 
addition, in this case the SCO cover all the eligible cost of the operation offering maximum simplification. 

130 

 
Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

The study has also assessed the feasibility of developing EU-level result-based tools (i.e. FNLC) in three areas. 
The  first  area  is  sustainable  multimodal  urban  mobility,  more  specifically  in  cycling  infrastructure,  clean 
urban transport rolling stock, and clean urban transport infrastructure. After compiling a list of result-based 
indicators monitored by projects funded by ERDF/CF in the selected investment area for the 2021-2027 period, 
we have identified three specific indicators (i.e. annual users of dedicated cycling infrastructure, annual users 
of new or modernised public transport and annual users of new or modernised tram and metro lines). These 
indicators are being tracked by Member States in the current period and are aligned with key EU strategies. 
Furthermore, they are directly relevant to the area under investigation and offer the potential for a target-based 
approach based on the number of new users of a new or modernised mode of transport. Regarding the potential 
data sources to establish financial amounts, the study team identified four sources, where the most viable was 
the publication ‘Handbook on the external costs of transport’. 

The second feasibility check was carried out in the area of skills for smart specialisation and transition, more 
specifically  the  assessment  focused  on  the  intervention  field  related  to  skills  development  for  smart 
specialisation, industrial transition, entrepreneurship, and adaptability of enterprises to change.  Preliminary 
desk  research  showed  that  the  activities  conducted  in  this  context  mainly  consist  of  training  and 
apprenticeships  aimed  at  enhancing  worker  competences  across  different  fields.  It  also  emerged  from  the 
analysis that the result indicator ‘SMEs staff completing training for skills for smart specialisation, for industrial 
transition and entrepreneurship’ (RCR98), is  the most adopted by MS in the current programming period to 
monitor these types of activities To establish financial amounts for the potential FNLC scheme, the study team 
identified the 'Continuing Vocational Training Survey (CVTS)' conducted by Eurostat in all EU Member States 
as a potential data source. Utilising data from this survey, two target-based approaches were considered: one 
based on the number of participants completing training in an SME linked to the RCR98 indicator, and the other 
one based on the number of employees completing one or more trainings in a given year.  

We  also  assessed  the  feasibility  of  developing  FNLC  in  the  area  of  climate  change  adaptation  and  risk 
prevention. By focusing on the prevention or management of floods and landslides, fires, other climate-related 
risks  and  non-climate  and  human-induced  risk,  we  were  able  to  identify  four  common  indicators  currently 
tracked  by  Member  States.  They  are:  population  benefiting  from  flood  protection  measures;  population 
benefiting from protection measures against climate-related natural disasters (other than flood and wildfires); 
population benefiting from wildfire protection measures; and population benefiting from protection measures 
against non-climate-related natural risks and risks related to human activities. Emergency events databases, 
such  as  the  EM-DAT,  were  considered  useful  in  assigning  financial  values  to  the  indicators  in  the  area, 
providing data that can be useful for the development of an FNLC based on avoidance costs. Alternatively, 
amounts could be assigned inspired by Croatia's National Recovery and Resilience Plan, which had its budget 
released upon the completion of a set of predefined targets (among them achieving a specific number of people 
benefiting from flood protection measures). 

131 

 
Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

Annex 1. Historical data collected 
from the Member States 

This annex contains the historical data collected from Member States, the subsequent calculations which were 
conducted based on historical data and statistical inferences extrapolations. It is submitted alongside the Final 
Study Report as separate MS Excel files for Areas 1-3. 

132 

 
Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

Annex 2. Operational Programmes 
sampled for historical data collected  

Table 37. Data collection process for Area 1 

MEMBER 

OPERATIONAL PROGRAMME 

STATUS 

STATE 

AT 

BE 

BE 

BG 

BG 

CY 

CZ 

CZ 

DE 

DE 

DE 

DE 

DE 

DE 

DK 

EE 

ES 

ES 

ES 

ES 

ES 

Investments in Growth and Employment - AT - ERDF 

Responded - data collection in 

Flanders - ERDF 

Brussels - ERDF 

Regions in Growth - BG - ERDF 

progress 

Data submitted to Core Team 

Data submitted to Core Team 

Data submitted to Core Team 

Innovations and Competitiveness - BG - ERDF 

Data submitted to Core Team 

Competitiveness and sustainable development - CY - ERDF/CF 

Data submitted to Core Team 

Integrated Regional Programme - CZ - ERDF 

Data submitted to Core Team 

Environment  - CZ - ERDF/CF 

Bremen - ERDF 

Rheinland-Pfalz - ERDF 

Sachsen - ERDF 

Data submitted to Core Team 

Data submitted to Core Team 

Data submitted to Core Team 

Data submitted to Core Team 

Mecklenburg-Vorpommern  - ERDF 

Data submitted to Core Team 

Baden-Württemberg  - ERDF 

Schleswig-Holstein - ERDF 

Data submitted to Core Team 

Data submitted to Core Team 

Innovation and Sustainable Growth in Businesses - DK - ERDF 

Data submitted to Core Team 

Cohesion Policy Funding - EE - ERDF/ESF/CF 

Responded - data collection in 

Canarias - ERDF 

Galicia  - ERDF 

Extremadura  - ERDF 

Andalucía  - ERDF 

Madrid  - ERDF 

progress 

Responded - data collection in 

progress 

Data submitted to Core Team 

Not responded, will not 

provide data 

Data submitted to Core Team 

Data submitted to Core Team 

133 

 
Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

MEMBER 

OPERATIONAL PROGRAMME 

STATUS 

STATE 

ES 

ES 

FI 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

GR 

GR 

GR 

GR 

GR 

HR 

HU 

HU 

IE 

IE 

IT 

IT 

IT 

IT 

IT 

IT 

Melilla  - ERDF 

Castilla y León  - ERDF 

Data submitted to Core Team 

Data submitted to Core Team 

Sustainable growth and jobs 2014-2020 - Structural Funds 

Responded, will not provide 

Programme of Finland [FI] 

data 

Provence-Alpes-Côte d'Azur - ERDF/ESF/YEI 

Data submitted to Core Team 

Nord-Pas de Calais - ERDF/ESF/YEI 

Data submitted to Core Team 

Aquitaine - ERDF/ESF/YEI 

Guadeloupe - ERDF/ESF/YEI 

Limousin - ERDF/ESF 

Lorraine et Vosges - ERDF/ESF/YEI 

Data submitted to Core Team 

Data submitted to Core Team 

Data submitted to Core Team 

Data submitted to Core Team 

Champagne-Ardenne - ERDF/ESF/YEI 

Data submitted to Core Team 

Martinique - ERDF/ESF/YEI 

Picardie - ERDF/ESF/YEI 

Réunion – ERDF 

Responded - data collection in 

progress 

Data submitted to Core Team 

Data submitted to Core Team 

Languedoc-Roussillon - ERDF/ESF/YEI 

Data submitted to Core Team 

Epirus - ERDF/ESF 

Pelonnesus - ERDF/ESF 

Crete - ERDF/ESF 

Thessaly - ERDF/ESF 

Data submitted to Core Team 

Data submitted to Core Team 

Data submitted to Core Team 

Data submitted to Core Team 

Competitiveness Entrepreneurship and Innovation - GR - ERDF/ESF  Data submitted to Core Team 

Competitiveness and Cohesion  - HR - ERDF/CF 

Data submitted to Core Team 

Economic Development and Innovation Programme - HU - 

Data submitted to Core Team 

ERDF/ESF/YEI 

Competitive Central-Hungary  - ERDF/ESF 

Data submitted to Core Team 

Border Midland and Western Regional - ERDF 

Data submitted to Core Team 

 Southern & Eastern Regional Operational Programmel - ERDF 

Data submitted to Core Team 

Lazio - ERDF 

Toscana - ERDF 

Friuli-Venezia Giulia - ERDF 

Molise  - ERDF/ESF 

Puglia  - ERDF/ESF 

Abruzzo - ERDF 

Data submitted to Core Team 

Responded - data collection in 

progress 

Data submitted to Core Team 

Data submitted to Core Team 

Data submitted to Core Team 

Data submitted to Core Team 

134 

 
Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

MEMBER 

OPERATIONAL PROGRAMME 

STATUS 

STATE 

IT 

IT 

IT 

LT 

LU 

LV 

MT 

NL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PT 

PT 

RO 

SE 

SE 

SI 

SK 

Liguria - ERDF 

Veneto - ERDF 

Valle d'Aosta - ERDF 

Data submitted to Core Team 

Data submitted to Core Team 

Data submitted to Core Team 

EU Structural Funds Investments - LT - ERDF/ESF/CF/YEI 

Data submitted to Core Team 

Luxembourg - ERDF 

Data submitted to Core Team 

Growth and Employment - LV - ERDF/ESF/CF/YEI 

Data submitted to Core Team 

Fostering a competitive and sustainable economy - MT - ERDF/CF 

Data submitted to Core Team 

West Netherlands - ERDF 

Responded, will not provide 

data 

Podkarpackie Voivodeship - ERDF/ESF 

Responded, will not provide 

data 

Lubuskie Voivodeship - ERDF/ESF 

Data submitted to Core Team 

Dolnośląskie Voivodeship - ERDF/ESF 

Data submitted to Core Team 

Opolskie Voivodeship - ERDF/ESF 

Lubelskie Voivodeship - ERDF/ESF 

Data submitted to Core Team 

Data submitted to Core Team 

Wielkolskie Voivodeship - ERDF/ESF 

Responded 

Zachodniomorskie Voivodeship - ERDF/ESF 

Data submitted to Core Team 

Mazowieckie Voivodeship - ERDF/ESF 

Data submitted to Core Team 

Norte - ERDF/ESF 

Azores - ERDF/ESF 

Data submitted to Core Team 

Data submitted to Core Team 

Integrated Regional Programme - RO - ERDF 

Data submitted to Core Team 

South Sweden - ERDF 

East-Central Sweden - ERDF 

Data submitted to Core Team 

Data submitted to Core Team 

EU Cohesion Policy - SI - ERDF/ESF/CF/YEI 

Data submitted to Core Team 

Quality of Environment - SK - ERDF/CF 

Data submitted to Core Team 

Additional OPs 

CZ 

HU 

HU 

PL 

PL 

PT 

FR 

Prague Growth Pole 

Data submitted to Core Team 

Territorial and Urban Development Operative Program  

Data submitted to Core Team 

Environment and Energy Efficiency Operational Programme 

Data submitted to Core Team 

Malopolskie Voivodeship  

Pomorskie Voivodeship 

Seur 

Alsace 

Data submitted to Core Team 

Data submitted to Core Team 

Data submitted to Core Team 

Data submitted to Core Team 

135 

 
Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

MEMBER 

OPERATIONAL PROGRAMME 

STATUS 

STATE 

FR 

Poitou-Charentes 

Data submitted to Core Team 

Source: prepared by the study team. 
Note: The initial sample of OPs selected for historical data collection was updated in the process to include eight additional OPs. It was 
done to mitigate the risk of insufficient data in countries where responses to our inquiries were slow or even absent. 

Table 38. Data collection process for Area 2 

MEMBER 

OPERATIONAL PROGRAMME 

STATUS 

STATE 

AT 

BE 

BE 

BG 

CY 

CZ 

CZ 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DK 

EE 

ES 

ES 

ES 

ES 

ES 

Investments in Growth and Employment - AT - ERDF 

Responded - data collection in 

Flanders - ERDF 

Brussels - ERDF 

progress 

Data submitted to Core Team 

Data submitted to Core Team 

Innovations and Competitiveness - BG - ERDF 

Data submitted to Core Team 

Competitiveness and sustainable development - CY - ERDF/CF 

Data submitted to Core Team 

Enterprise and Innovation for Competitiveness - CZ - ERDF 

Data submitted to Core Team 

Research Development and Education - CZ - ESF/ERDF 

Data submitted to Core Team 

Bremen - ERDF 

Rheinland-Pfalz - ERDF 

Sachsen - ERDF 

Data submitted to Core Team 

Data submitted to Core Team 

Data submitted to Core Team 

Mecklenburg-Vorpommern  - ERDF 

Data submitted to Core Team 

Baden-Württemberg  - ERDF 

Schleswig-Holstein - ERDF 

Hamburg - ERDF 

Responded - no relevant data 

available 

Data submitted to Core Team 

Data submitted to Core Team 

Innovation and Sustainable Growth in Businesses - DK - ERDF 

Data submitted to Core Team 

Cohesion Policy Funding - EE - ERDF/ESF/CF 

Data submitted to Core Team 

Canarias - ERDF 

Galicia  - ERDF 

Extremadura  - ERDF 

Andalucía  - ERDF 

Madrid - ERDF 

Not responded 

Data submitted to Core Team 

Responded - No relevant 

interventions funded 

Not responded 

Responded - data collection in 

progress 

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MEMBER 

OPERATIONAL PROGRAMME 

STATUS 

STATE 

ES 

ES 

FI 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

GR 

GR 

HR 

HU 

HU 

IE 

IE 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

Melilla - ERDF 

Castilla y León  - ERDF 

Responded - No relevant 

interventions funded 

Responded - No relevant 

interventions funded 

Sustainable growth and jobs 2014-2020 - Structural Funds 

Data submitted to Core Team 

Programme of Finland [FI] 

Provence-Alpes-Côte d'Azur - ERDF/ESF/YEI 

Data submitted to Core Team 

Nord-Pas de Calais - ERDF/ESF/YEI 

Responded - data collection in 

progress 

Aquitaine - ERDF/ESF/YEI (Nouvelle-Aquitaine) 

Data submitted to Core Team 

Guadeloupe - ERDF/ESF/YEI 

Data submitted to Core Team 

Limousin - ERDF/ESF (Nouvelle-Aquitaine) 

Data submitted to Core Team 

Lorraine et Vosges - ERDF/ESF/YEI (Grand Este) 

Responded - No relevant 

interventions funded 

Champagne-Ardenne - ERDF/ESF/YEI (Grand Este) 

Data submitted to Core Team 

Réunion – ERDF 

Data submitted to Core Team 

Languedoc-Roussillon - ERDF/ESF/YEI (Occitanie) 

Data submitted to Core Team 

Epirus - ERDF/ESF 

Data submitted to Core Team 

Competitiveness Entrepreneurship and Innovation - GR - ERDF/ESF  Data submitted to Core Team 

Competitiveness and Cohesion  - HR - ERDF/CF 

Data submitted to Core Team 

Economic Development and Innovation Programme - HU - 

Data submitted to Core Team 

ERDF/ESF/YEI 

Competitive Central-Hungary  - ERDF/ESF 

Data submitted to Core Team 

Border Midland and Western Regional - ERDF 

Not responded 

 Southern & Eastern Regional Operational Programmel - ERDF 

Data submitted to Core Team 

Lazio - ERDF 

Toscana - ERDF 

Friuli-Venezia Giulia - ERDF 

Molise  - ERDF/ESF 

Puglia  - ERDF/ESF 

Responded - data collection in 

progress 

Data submitted to Core Team 

Responded - data collection in 

progress 

They cannot manage to provide 

us data 

Data submitted to Core Team 

Abruzzo – ERDF                                       

Not responded 

Liguria - ERDF 

Data submitted to Core Team 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

MEMBER 

OPERATIONAL PROGRAMME 

STATUS 

STATE 

IT 

IT 

LT 

LU 

LV 

MT 

NL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PT 

PT 

RO 

RO 

SE 

SI 

SK 

Veneto - ERDF 

Valle d'Aosta - ERDF 

Data submitted to Core Team 

Data submitted to Core Team 

EU Structural Funds Investments - LT - ERDF/ESF/CF/YEI 

Data submitted to Core Team 

Luxembourg - ERDF 

Data submitted to Core Team 

Growth and Employment - LV - ERDF/ESF/CF/YEI 

Data submitted to Core Team 

Fostering a competitive and sustainable economy - MT - ERDF/CF 

Data submitted to Core Team 

South Netherlands- ERDF 

Responded - data collection 

only in September possible 

Podkarpackie Voivodeship - ERDF/ESF 

Data submitted to Core Team 

Lubuskie Voivodeship - ERDF/ESF 

Data submitted to Core Team 

Dolnośląskie Voivodeship - ERDF/ESF 

Data submitted to Core Team 

Opolskie Voivodeship - ERDF/ESF 

Data submitted to Core Team 

Lubelskie Voivodeship - ERDF/ESF 

Data submitted to Core Team 

Wielkolskie Voivodeship - ERDF/ESF 

Data submitted to Core Team 

Zachodniomorskie Voivodeship - ERDF/ESF 

Data submitted to Core Team 

Mazowieckie Voivodeship - ERDF/ESF 

Data submitted to Core Team 

Norte - ERDF/ESF 

Azores - ERDF/ESF 

Data submitted to Core Team 

Data submitted to Core Team 

Competitiveness Programme - RO - ERDF 

Data submitted to Core Team 

Regional OP 

South Sweden - ERDF 

Data submitted to Core Team 

Data submitted to Core Team 

EU Cohesion Policy - SI - ERDF/ESF/CF/YEI 

Data submitted to Core Team 

Integrated Infrastructure - SK - ERDF/CF 

Data submitted to Core Team 

ADDITIONAL OPS 

FR 

FR 

Midi-Pyrénées (Occitanie) 

Data submitted to Core Team 

Poitou-Charentes  (Nouvelle-Aquitaine) 

Data submitted to Core Team 

Source: prepared by the study team. 

Table 39. Data collection progress for Area 3 

MEMBER 

OPERATIONAL PROGRAMME 

STATUS 

STATE 

AT 

Investments in Growth and Employment - AT - ERDF 

Data submitted to Core Team 

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MEMBER 

OPERATIONAL PROGRAMME 

STATUS 

STATE 

BE 

BE 

BG 

CY 

CZ 

CZ 

DE 

DE 

DE 

DE 

DE 

DE 

DK 

EE 

ES 

ES 

ES 

ES 

ES 

ES 

ES 

ES 

FI 

FR 

FR 

Flanders - ERDF 

Brussels - ERDF 

Data submitted to Core Team 

Responded - no relevant data 

available 

Innovations and Competitiveness - BG - ERDF 

Data submitted to Core Team 

Competitiveness and sustainable development - CY - ERDF/CF 

Responded - no relevant data 

available 

Enterprise and Innovation for Competitiveness - CZ - ERDF 

Data submitted to Core Team 

Research Development and Education - CZ - ESF/ERDF 

Data submitted to Core Team 

Bremen - ERDF 

Rheinland-Pfalz - ERDF 

Sachsen - ERDF 

Responded - no relevant data 

available 

Responded - no relevant data 

available 

Data submitted to Core Team 

Mecklenburg-Vorpommern  - ERDF 

Data submitted to Core Team 

Baden-Württemberg  - ERDF 

Schleswig-Holstein - ERDF 

Responded - no relevant data 

available 

Responded - no relevant data 

available 

Innovation and Sustainable Growth in Businesses - DK - ERDF 

Data submitted to Core Team 

Cohesion Policy Funding - EE - ERDF/ESF/CF 

Data submitted to Core Team 

Canarias - ERDF 

Galicia  - ERDF 

Extremadura  - ERDF 

Andalucía  - ERDF 

Madrid  - ERDF 

Melilla  - ERDF 

Castilla y León  - ERDF 

Responded - no relevant data 

available 

Data submitted to Core Team 

Responded - no relevant data 

available 

Not possible to provide data 

Not possible to provide data 

Responded - no relevant data 

available 

Data submitted to Core Team 

Multiregional OP for Spain ERDF 2014-20 

Data submitted to Core Team 

Sustainable growth and jobs 2014-2020 - Structural Funds 

Data submitted to Core Team 

Programme of Finland [FI] 

Provence-Alpes-Côte d'Azur - ERDF/ESF/YEI 

Responded - no relevant data 

available 

Nord-Pas de Calais - ERDF/ESF/YEI (Hauts-de-France) 

Not possible to provide data 

139 

 
Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

MEMBER 

OPERATIONAL PROGRAMME 

STATUS 

STATE 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

GR 

GR 

GR 

GR 

GR 

HR 

HU 

HU 

IE 

IE 

IT 

IT 

IT 

Aquitaine - ERDF/ESF/YEI (Nouvelle-Aquitaine) 

Responded - no relevant data 

Guadeloupe - ERDF/ESF/YEI 

available 

No relevant interventions 

funded 

Limousin - ERDF/ESF (Nouvelle-Aquitaine) 

Responded - no relevant data 

available 

Lorraine et Vosges - ERDF/ESF/YEI (Grand Este) 

Data submitted to Core Team 

Champagne-Ardenne - ERDF/ESF/YEI (Grand Este) 

Responded - no relevant data 

Alsace (Grand Este) 

Martinique - ERDF/ESF/YEI 

available 

Data submitted to Core Team 

Not possible to provide data 

Picardie - ERDF/ESF/YEI (Hauts-de-France) 

Not possible to provide data 

Réunion – ERDF 

Languedoc-Roussillon - ERDF/ESF/YEI (Occitanie) 

Epirus - ERDF/ESF 

Pelonnesus - ERDF/ESF 

Crete - ERDF/ESF 

Thessaly - ERDF/ESF 

Data submitted to Core Team 

Responded - No relevant 

interventions funded 

Responded - no relevant data 

available 

Responded - no relevant data 

available 

Responded - no relevant data 

available 

Responded - no relevant data 

available 

Competitiveness Entrepreneurship and Innovation - GR - ERDF/ESF  Data submitted to Core Team 

Competitiveness and Cohesion - HR - ERDF/CF 

Data submitted to Core Team 

Economic Development and Innovation Programme - HU - 

Data submitted to Core Team 

ERDF/ESF/YEI 

Competitive Central-Hungary - ERDF/ESF 

Responded - no relevant data 

available 

Border Midland and Western Regional - ERDF 

Responded - no relevant data 

available 

Southern & Eastern Regional Operational Programme - ERDF 

Responded - no relevant data 

Lazio - ERDF 

Toscana - ERDF 

Friuli-Venezia Giulia - ERDF 

available 

Not possible to provide data 

Data submitted to Core Team 

Not possible to provide data 

140 

 
Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

MEMBER 

OPERATIONAL PROGRAMME 

STATUS 

STATE 

IT 

IT 

IT 

IT 

IT 

IT 

LT 

LU 

LV 

MT 

NL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PT 

PT 

PT 

Molise  - ERDF/ESF 

Puglia  - ERDF/ESF 

Abruzzo - ERDF 

Liguria - ERDF 

Veneto - ERDF 

Valle d'Aosta - ERDF 

Responded - no relevant data 

available 

Data submitted to Core Team 

Not possible to provide data 

Responded - no relevant data 

available 

Data submitted to Core Team 

Responded - no relevant data 

available 

EU Structural Funds Investments - LT - ERDF/ESF/CF/YEI 

Data submitted to Core Team 

Luxembourg - ERDF 

Responded - no relevant data 

available 

Growth and Employment - LV - ERDF/ESF/CF/YEI 

Data submitted to Core Team 

Fostering a competitive and sustainable economy - MT - ERDF/CF 

Data submitted to Core Team 

South Netherlands - ERDF 

Responded – no relevant data 

available 

Podkarpackie Voivodeship - ERDF/ESF 

Responded - no relevant data 

available 

Lubuskie Voivodeship - ERDF/ESF 

Responded - no relevant data 

available 

Dolnośląskie Voivodeship - ERDF/ESF 

Data submitted to Core Team 

Opolskie Voivodeship - ERDF/ESF 

Lubelskie Voivodeship - ERDF/ESF 

Responded - no relevant data 

available 

Responded - no relevant data 

available 

Wielkolskie Voivodeship - ERDF/ESF 

Responded - no relevant data 

available 

Zachodniomorskie Voivodeship - ERDF/ESF 

Data submitted to Core Team 

Mazowieckie Voivodeship - ERDF/ESF 

Responded - no relevant data 

Slaskie Voivodeship 

Malopolskie Voivodeship 

Norte - ERDF/ESF 

Azores - ERDF/ESF 

Centro 

available 

Data submitted to Core Team 

Data submitted to Core Team 

Data submitted to Core Team 

Responded – not relevant data 

available 

Data submitted to Core Team 

141 

 
Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

MEMBER 

OPERATIONAL PROGRAMME 

STATUS 

STATE 

PT 

PT 

PT 

PT 

RO 

RO 

SE 

SE 

SI 

SK 

Algarve 

Lisboa 

Alentejo 

Data submitted to Core Team 

Data submitted to Core Team 

Data submitted to Core Team 

Competitiveness and Internationalisation OP 

Data submitted to Core Team 

Integrated Regional Programme - RO - ERDF 

Responded - no relevant data 

Regional OP 

South Sweden - ERDF 

East-Central Sweden - ERDF 

available 

Not possible to provide data 

Data submitted to Core Team 

Responded - no relevant data 

available 

EU Cohesion Policy - SI - ERDF/ESF/CF/YEI 

Data submitted to Core Team 

Integrated Infrastructure - SK - ERDF/CF 

Responded  - cannot provide 

the requested data detail 

Source: prepared by the study team.

142 

 
Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

Annex 3. Data quality assessment of 
Areas 1, 2 and 3 

The assessment of historical data involves checking the following data points due to their necessity for the EU-
level SCO development process, namely: 

• 

information on activities supported in implemented projects: these data are important for 
comparing the interventions supported by the Member State over time and across different 
Member States; 

•  data on costs incurred in implemented projects, broken down by activity and by category of costs: 
these data are important for attribution of incurred costs, determination of a standard set of cost 
items included in the definition of a SCO, identification of outlier cases, elimination of gaps in the 
cost breakdown data, etc.; 

•  data on outputs and results delivered in implemented projects: these data are important for 
estimation and attribution of the monetary value to indicators used when developing SCOs, 
especially standard scales of unit costs. 

We also present the alternative sources which have been explored in order to aid the development of EU-level 
SCOs.  

Energy Efficiency & Renewable Energy 

Data collected from Member States 

As  far  as  the  availability  of  the  collected  data  are  concerned,  Table  40  offers  a  complete  overview  of  the 
information gathered at the level of the three sub-areas analysed in this report. It indicates the number of OPs 
and projects / calls for proposals for which relevant data were provided in each Member State: 

• 

• 

Sub-area 1A (energy efficiency and renewable energy measures in SMEs – EE-RES SMEs): data on 
26 OPs (46 % of sampled OPs) from 15 Member States were made available to the study team, 
which amounts to 429 records on specific projects or calls for proposals.   
Sub-area 1B.1 (energy efficiency and renewable energy measures in housing sector – EE-RES 
Housing): data on 33 OPs (58 % of sampled OPs) from 16 Member States were collected, which 
amounts to 893 records referring to either specific projects or calls for proposals. 

•  Sub-area 1B.2 (energy efficiency and renewable energy measures in non-residential buildings – EE-

RES non-residential): data on 36 OPs (63 % of sampled OPs) were provided by 15 Member States, 
which amounts to 1972 records on specific projects or calls for proposals. 

Please note that some figures on recorded projects / calls for proposals may be inflated by inclusion of smaller 
projects, as is evident in the case of Croatian projects. 

143 

 
Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

Table 40. Overview of data coverage per sub-area in Area 1 

MS 

AT 

BE 

BG 

CY 

CZ 

DE 

DK 

EE 

ES 

FI 

FR 

GR 

HR 

HU 

IE 

IT 

LT 

LU 

LV 

MT 

NL 

PL 

PT 

RO 

SE 

SI 

SK 

EE-RES SMES 

EE-RES HOUSING 

EE-RES NON-RESIDENTIAL 

N. OP 

N. 
PROJECT/CALL 

N. OP 

N.PROJ/CALL 

N. OP 

N.PROJ/CALL 

1 

1 

5 

1 

1 

5 

1 

1 

2 

1 

1 

3 

1 

1 

1 

4 

1 

10 

19 

79 

59 

194 

1 

25 

7 

1 

19 

3 

1 

6 

1 

1 

1 

1 

12 

1 

1 

2 

2 

1 

1 

1 

1 

6 

1 

1 

7 

17 

1 

3 

212 

3 

585 

2 

7 

1 

2 

1 

1 

27 

23 

1 

1 

1 

4 

2 

7 

1 

2 

3 

1 

1 

1 

7 

3 

1 

1 

1 

19 

756 

57 

112 

871 

3 

11 

10 

2 

4 

89 

32 

1 

4 

Source: prepared by the study team. 

Quality-wise,  we  checked  if  all  key  data  points  necessary  for  the  SCO  development  were  provided  in  the 
requested format. The key issue of the collected data is that it lacked a detailed breakdown into specific activities 
and cost categories: data providers often indicated which categories of costs  were covered under the specific 
activities, but in most cases were unable to provide a breakdown of amounts spent in each category.  

Another  important  limitation  of  these  data  was  the  inconsistency  or  variety  of  supported  activities  across 
analysed  Member  States,  even  within  the  same  sub-area.  Building  on  this  observation,  we  compiled  an 

144 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

overview of supported activities for each Member State, facilitating a comparison of the scope of activities across 
countries and selection of the most relevant activities for each of the three sub-areas.  

The historical data on outputs or results of implemented projects has shown that ERDF output indicators had 
the widest coverage of implementation in Member States and was therefore shortlisted for further development.  

Most of the historical data were provided at the level of individual projects: 91 % of records for sub-area 1A, 
95 % for sub-area 1B.1, and 94 % for sub-area 1B.2 were available at this level of granularity. Only 7 % of records 
were available at the level of calls for proposals in the case for sub-area 1A, while the same could be said for 
3 % of records in sub-area 1B.1 and 4 % of records in sub-area 1B.2.  For the remaining 2 % of records in each 
sub-area, the level of granularity could not be clearly identified because either it was not specified, or the data 
were provided at various levels of detail. 

Only a small portion of the cost data provided to the study relate to ongoing projects: 17 % in sub-area 1A, 7 % 
in sub-area 1B.1, and 6 % in sub-area 1B.2. Instead, 81 % of records in sub-area 1A, 89 % in sub-area 1B.1, and 
90 % in sub-area 1B.2 refer to completed projects. In 3 % of available records for sub-area 1A and 4 % of both 
sub-areas 1B.1 and 1B.2 it was not possible to clearly define the progress level of selected projects due to unclear 
or incomplete information. 

Most of the projects / calls for proposals were implemented during the last 3 years, i.e. in the period 2019-2021. 
In some of the cases, however, the reference period was unspecified – the historical data in question refer to the 
entire programming period of 2014-2020.  

Typologies of activities according to collected data 

Based  on  the  analysis  of  the  qualitative  information  gathered  in  sub-area  1A,  at  least  3  Member  States 
implemented (almost) the full set of activities identified in the DCF (see Table 41). Activities funded in other 
Member States at least to some degree seemed to overlap with this typology. However, compared to Germany, 
Italy or Poland, countries such as Belgium, Bulgaria, Denmark or Sweden funded a significantly narrower sub-
set of activities. 

The most common / most frequently overlapping activities funded in most of the analysed Member States were 
the following: 

• 
• 
• 

advice consultancy to SME owners on energy efficiency and potential for renewable energy; 
energy audits to identify, quantify and report existing energy consumption profiles; 
installation of photovoltaic system in SMEs. 

In the case of Croatia, authorities responsible for the Competitiveness and Cohesion OP were not able to provide 
disaggregated information on the typology of activities supported in sub-area 1A. However, from the analysis 
of  the  eligibility  rules  of  the  relevant  calls  for  which  data  were  provided  it  emerged  that  all  the  activities 
included in the list could potentially be funded under this OP. 

In sub-area 1B.1, only Greece and Poland funded all activities65 identified in the DCF (see Table 42). Belgium, 
Bulgaria, Latvia, Lithuania, Luxembourg, and Malta implemented projects where only a small sub-set of these 

65 Information on activities funded in Croatia, like in sub-area 1A, is limited. However, based on the analysis of the eligibility rules of the relevant calls for 
proposals, all activities listed in the DCF were eligible in implemented projects. 

145 

 
 
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activities were funded. Other Member States covered a wider range of activities more akin to activities funded 
in Greece and Poland. 

Despite the observed variation in typologies, a significant number of mapped activities seemed to overlap at 
least  to  some  degree  in  projects  implemented  in  the  analysed  Member  States.  The  most  common  /  most 
frequently overlapping activities were as follows: 

energy audits for buildings; 
replacement of window frames / glass / moving of fixed shading systems; 

• 
• 
•  upgrade of thermal insulation (walls, roofs, ceiling, etc.); 
• 

installation of new high efficiency or upgrade of existing heating/cooling systems (including based 
on RES); 
installation of hot water system with the use of RES; 
installation of renewable electricity unit; 
consultancy service for preparing the necessary technical studies / reports as well as monitoring 
the implementation of the interventions. 

• 
• 
• 

In sub-area 1B.2, the analysis revealed that all activities included in the DCF (see Table 43) were implemented 
by Germany, Poland and Portugal. A few other Member States funded a slightly smaller sub-set of activities. 
Belgium, Bulgaria, and Croatia, on the other hand, implemented much smaller projects where only one or two 
activities were funded. 

Overall, based on the available data, the most common / most frequently overlapping activities in sub-area 1B.2 
were largely identical to activities funded in sub-area 1B.1: 

energy audits for buildings; 
replacement of window frames / glass / moving of fixed shading systems; 

• 
• 
•  upgrade of thermal insulation (walls, roofs, ceiling, etc.); 
• 

installation of new high efficiency or upgrade of existing heating/cooling systems (including based 
on RES); 
installation of hot water system with the use of RES; 
installation of renewable electricity unit; 
smart management systems; 
consultancy service for preparing the necessary technical studies/reports as well as monitoring the 
implementation of the interventions. 

• 
• 
• 
• 

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Table 41. Typologies of activities supported in sub-area 1A 

Member 
State 

Advice 
consultancy to 
SME owners on 
energy efficiency 
and potential for 
renewable energy 

Energy audits to 
identify, quantify 
and report 
existing energy 
consumption 
profiles 

Replacement of 
window 
frames/glass/fixed 
shading systems in 
SMEs 

Upgrade of 
thermal 
insulation 
in SMEs 

Upgrade of 
heating/cooling 
systems (including 
based on RES) in 
SMEs 

Installation of 
hot water 
system with 
the use of RES 
in SMEs 

Installation of 
photovoltaic 
system in 
SMEs 

Installation of 
biomass 
energy 
system in 
SMEs 

Energy 
storage 
systems 
in SMEs 

Smart 
management 
systems in 
SMEs 

Consultancy service for 
the preparation of the 
necessary technical 
studies/reports as well as 
monitoring of the 
implementation of the 
interventions in SMEs 

Energy 
efficiency 
upgrade of 
production 
equipment for 
groups of 
SMEs 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

BE 

BG 

DE 

DK 

ES 

FR 

HR 

HU 

IT 

LT 

LV 

PL 

PT 

SE 

SI 

Source: prepared by the study team. 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

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Table 42. Typologies of activities supported in sub-area 1B.1 

Member State 

Energy audits for 
buildings 

Replacement of window 
frames/glass/moving of fixed 
shading systems 

Upgrade of 
thermal 
insulation (walls, 
roofs, ceiling, 
etc.) 

Installation of new 
high efficiency or 
upgrade of existing 
heating/cooling 
systems (including 
based on RES) 

Installation of hot 
water system 
with the use of 
RES 

Installation of 
renewable 
electricity unit 

Installation of 
biomass energy 
system 

Energy storage 
systems 

Smart 
management 
systems 

Consultancy service 
for the preparation of 
the necessary 
technical 
studies/reports as 
well as monitoring of 
the implementation 
of the interventions 

BE 

BG 

CY 

CZ 

FR 

GR 

HR 

HU 

IE 

LT 

LU 

LV 

MT 

PL 

PT 

RO 

x 

x 

x 

x 

x 

x 

x 

x 

x 

Source: prepared by the study team.

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

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Table 43. Typologies of activities supported in sub-area 1B.2 

Member State 

Energy audits for 
buildings 

Replacement of window 
frames/glass/moving of fixed 
shading systems 

Upgrade of 
thermal 
insulation (walls, 
roofs, ceiling, 
etc.) 

Installation of new 
high efficiency or 
upgrade of existing 
heating/cooling 
systems (including 
based on RES) 

Installation of hot 
water system 
with the use of 
RES 

Installation of 
renewable 
electricity unit 

Installation of 
biomass energy 
system 

Energy storage 
systems 

Smart 
management 
systems 

Consultancy service 
for the preparation of 
the necessary 
technical 
studies/reports as 
well as monitoring of 
the implementation 
of the interventions 

SK 

BE 

BG 

DE 

ES 

FR 

HR 

HU 

IT 

LU 

LV 

MT 

PL 

PT 

RO 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

Source: prepared by the study team.

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

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Costs incurred according to collected data 

This section offers an overview of the analysis of the activities for which data on the total cost breakdown at 
activity level were provided.  

As can be observed, Table 44 below shows a lower number of reported activities per Member State than Table 
41. This is because many OPs were able to identify the types of activity funded under the relevant projects / 
calls but do not have activity-level disaggregated cost data available. 

Table 44 Overview of cost data quality in selected sub-areas 

Sub-area 1A 

Sub-area 1B.1 

Sub-area 1B.2 

A more detailed analysis revealed 
that 77 % of OPs provided data on 
costs breakdown at activity level, 
albeit at varying levels of coverage of 
the activities.  

On the basis of the available data it is 
possible to make an initial analysis of 
the composition of these operations 
among the different Member States. 
First of all, it can be observed how the 
different Member States are 
characterised by a certain degree of 
heterogeneity in terms of activities’ 
structuring of the operations. In 
particular, it is interesting to note 
how some countries covered almost 
all the activities through their 
interventions (e.g. Italy, Poland) 
while others just focused on 1 or 2 
typologies (e.g. Hungary, Latvia). 
Finally, according to the collected 
historical data the most funded 
activities, in the context of Area 1A 
were: 

A similar picture can be painted in 
the case of sub area 1B.1 – Member 
States were not able to provide 
disaggregated cost data at activity 
level. In the case of Belgium, Croatia, 
Hungary, Ireland, Latvia, Lithuania, 
and Romania only data on the total 
costs of the operation were available.   

More than half of Member States 
were able to provide this level of 
detail, although not always in a 
complete manner in terms of financial 
amounts. A more detailed analysis 
revealed that 57 % of OPs provided 
data on costs incurred at activity 
level. It is worth noting that only 19 % 
of the OPs which provided data at 
activity level did so for all activities 
under the sub area, meaning that we 
had few complete sets of data. In 
turn, this means that we had to rely 
on interpolations for those Member 
States which possess partial data. 

A slightly better situation was 
exhibited in sub area 1B.2 – while a 
many Member States were not able to 
provide disaggregated cost data at 
activity level, the availability of at 
least partial data on costs of activities 
were available and thus enabled the 
study team to conduct necessary 
interpolations to calculate values for 
Member States which do not have the 
necessary data on each activity based 
on the available data sample.  The 
analysis of availability of cost 
breakdowns has revealed that 73 % of 
OPs provided data on costs 
breakdown at activity level, while in 
33 % of the programmes this level of 
information was provided for all the 
operations – namely, Bulgaria, Italy, 
Poland, Slovakia, and Spain. 

• 

• 

Energy audits to 
identify, quantify and 
report existing energy 
consumption profiles, 
and 
Installation of 
photovoltaic system in 
SMEs 

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Sub-area 1A 

Sub-area 1B.1 

Sub-area 1B.2 

‘CO34 – Estimated annual decrease of 
GHG’ adopted by 65 % of the OPs in 
10 Member States, or by 67 % of the 
Member States that provided data on 
relevant interventions. 

‘CO30 – Additional capacity of 
renewable energy production’ 
adopted by 42 % of the OPs in 8 
Member States, or by 53 % of the 
Member States that provided data on 
relevant interventions. 

‘CO31 – Households with improved 
energy consumption classification’ 
adopted by all 15 Member States in 
the sample. However, please note 
that without sufficient data 
availability on costs (that was 
described earlier) it was not possible 
to cover all of said OPs without 
making some assumptions. 

‘CO34 – Estimated annual decrease of 
GHG’ which was adopted by 11 
Member States, or 69 % of the 
Member States that provided data on 
relevant interventions. 

‘CO32 – Decrease of annual primary 
energy consumption of public 
buildings, in kilowatt-hours per year’ 
adopted by 63 % of Member States in 
the sample. The relatively wide 
availability of this indicator under 
sub-area 1B.1 can be explained by the 
presence of public housing initiatives. 
For the purpose of the current 
analysis, we included such initiatives 
under sub-area 1B.2. 

‘CO30 – Additional capacity of 

renewable energy production’ 
adopted by 7 Member States or by 

44 % of the collected sample.  

‘CO32 – Decrease of annual primary 
energy consumption of public 
buildings, in kilowatt-hours per year’ 
adopted by 93 % of the Member 
States in the sample  

‘CO34 – Estimated annual decrease of 
GHG’ adopted by 12 Member States 
or by 80 % of Member States in the 
sample 

‘CO31 – Households with improved 
energy consumption classification’ 
adopted by 47 % of the Member 
States in the sample. The relatively 
wide availability of this indicator 
under the sub-area can be explained 
by the presence of public housing 
initiatives, as was the case in the 
previous section on indicator CO32. 
For the purpose of the current 
analysis, we included such initiatives 
under sub area 1B.1 

‘CO30 – Additional capacity of 

renewable energy production’ 
adopted by 7 Member States or 47 % 
of the Member States in the sample.  

Sub-area 1A  

Sub-area 1B.1 

Sub-area 1B.2 

Data on 26 OPs (46 % of sampled OPs) 
from  15  Member  States  were  made 

Data on 33 OPs (58 % of sampled OPs) 
from 16 Member States were collected, 

Data on 36 OPs (63 % of sampled OPs) 
were  provided  by  15  Member  States, 

available  to  the  study  team,  which 
amounts  to  429  records  on  specific 

which  amounts 
records 
referring  to  either  specific  projects  or 

to  893 

which  amounts  to  1 972  records  on 
specific projects or calls for proposals. 

projects or calls for proposals.   

calls for proposals. 

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Table 45. Availability of the breakdown of costs at activity level in sub-area 1A   

Member State 

Breakdown of 
cost at activity 
level 

Advice 
consultancy 
to SME 
owners on 
energy 
efficiency 
and 
potential 
for 
renewable 
energy 

Energy audits 
to identify, 
quantify and 
report 
existing 
energy 
consumption 
profiles 

BE 

BG 

DE 

DK 

ES 

FR 

HR 

HU 

IT 

LT 

LV 

PL 

x 

x 

NO - ONLY 

TOT COSTS 

YES 

PARTIAL 

YES 

YES 

PARTIAL 

YES 

NO - ONLY 

TOT COSTS 

PARTIAL 

x 

NO - ONLY 

TOT COSTS 

YES 

PARTIAL 

x 

x 

x 

x 

x 

x 

Replacement of 
window 
frames/glass/fixed 
shading systems 
in SMEs 

Upgrade of 
thermal 
insulation in 
SMEs 

Upgrade of 
heating/cooling 
systems 
(including 
based on RES) 
in SMEs 

Installation of 
hot water 
system with 
the use of RES 
in SMEs 

Installation of 
photovoltaic 
system in 
SMEs 

Installation of 
biomass 
energy 
system in 
SMEs 

Energy 
storage 
systems in 
SMEs 

Smart 
management 
systems in 
SMEs 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

Consultancy 
service for 
preparing the 
necessary 
technical 
studies/reports 
as well as 
monitoring the 
implementation 
of the 
interventions in 
SMEs 

Energy 
efficiency 
upgrade of 
production 
equipment 
for groups 
of SMEs 

x 

x 

x 

x 

x 

x 

x 

x 

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Member State 

Breakdown of 
cost at activity 
level 

Advice 
consultancy 
to SME 
owners on 
energy 
efficiency 
and 
potential 
for 
renewable 
energy 

Energy audits 
to identify, 
quantify and 
report 
existing 
energy 
consumption 
profiles 

PT 

SE 

SI 

YES 

x 

NO - ONLY 

TOT COSTS 

YES 

Source: prepared by the study team. 

Replacement of 
window 
frames/glass/fixed 
shading systems 
in SMEs 

Upgrade of 
thermal 
insulation in 
SMEs 

Upgrade of 
heating/cooling 
systems 
(including 
based on RES) 
in SMEs 

Installation of 
hot water 
system with 
the use of RES 
in SMEs 

Installation of 
photovoltaic 
system in 
SMEs 

Installation of 
biomass 
energy 
system in 
SMEs 

Energy 
storage 
systems in 
SMEs 

Smart 
management 
systems in 
SMEs 

x 

x 

x 

Consultancy 
service for 
preparing the 
necessary 
technical 
studies/reports 
as well as 
monitoring the 
implementation 
of the 
interventions in 
SMEs 

Energy 
efficiency 
upgrade of 
production 
equipment 
for groups 
of SMEs 

x 

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Table 46. Availability of the breakdown of costs at activity level in sub-area 1B.1 

Member 
State 

Breakdown of 
cost at activity 
level 

Energy 
audits for 
buildings 

Replacement of window 
frames/glass/moving of fixed 
shading systems 

Upgrade of 
thermal 
insulation 
(walls, roofs, 
ceiling, etc.) 

Installation of new 
high efficiency or 
upgrade of existing 
heating/cooling 
systems (including 
based on RES) 

Installation of hot 
water system 
with the use of 
RES 

Installation of 
renewable 
electricity unit 

Installation of 
biomass energy 
system 

Energy 
storage 
systems 

Smart 
management 
systems 

Consultancy service 
for preparing the 
necessary technical 
studies/reports as 
well as monitoring 
the implementation 
of the interventions 

BE 

BG 

CY 

CZ 

FR 

GR 

HR 

HU 

IE 

LT 

LU 

LV 

MT 

PL 

PT 

RO 

x 

x 

x 

NO - ONLY 

TOT COSTS 

YES 

PARTIAL 

PARTIAL 

PARTIAL 

YES 

NO - ONLY 

TOT COSTS 

NO - ONLY 

TOT COSTS 

NO - ONLY 

TOT COSTS 

NO - ONLY 

TOT COSTS 

PARTIAL 

x 

NO - ONLY 

TOT COSTS 

PARTIAL 

YES 

PARTIAL 

NO - ONLY 

TOT COSTS 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

154 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

Source: prepared by the study team. 

Table 47. Availability of the breakdown of costs at activity level in sub-area 1B.2 

Member 
State 

Breakdown of 
cost at activity 
level 

Energy 
audits for 
buildings 

Replacement of window 
frames/glass/moving of fixed 
shading systems 

Upgrade of 
thermal 
insulation 
(walls, roofs, 
ceiling, etc.) 

Installation of new 
high efficiency or 
upgrade of existing 
heating/cooling 
systems (including 
based on RES) 

Installation of hot 
water system 
with the use of 
RES 

Installation of 
renewable 
electricity unit 

Installation of 
biomass energy 
system 

Energy 
storage 
systems 

Smart 
management 
systems 

Consultancy service 
for preparing the 
necessary technical 
studies/reports as 
well as monitoring 
the implementation 
of the interventions 

SK 

BE 

BG 

DE 

ES 

FR 

HR 

HU 

IT 

LU 

LV 

MT 

PL 

PT 

RO 

x 

x 

x 

x 

x 

x 

x 

x 

YES 

NO - ONLY 

TOT COSTS 

YES 

PARTIAL 

YES 

PARTIAL 

NO - ONLY 

TOT COSTS 

NO - ONLY 

TOT COSTS 

YES 

PARTIAL 

PARTIAL 

PARTIAL 

YES 

PARTIAL 

NO - ONLY 

TOT COSTS 

Source: prepared by the study team. 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

Outputs and results produced according to collected data 

From the analysis of outputs and results of the calls / projects for which data were collected, it emerged that 
common  ERDF  output  indicators  have  seen  wide  usage,  as  most  of  the  OPs  opted  for  the  use  of  common 
indicators in all three sub-areas. This indicates their suitability for EU-level SCO development. 

Of the 15 MS that provided data on sub-area 1A operations, only 3 did not adopt common output indicators, 
namely Belgium, Lithuania, and Spain. Table 48 shows that the most used indicators in this area of intervention 
were: 

• 

• 

‘CO34 – Estimated annual decrease of GHG’ adopted by 65 % of the OPs in 10 Member States, or 
by 67 % of the Member States that provided data on relevant interventions. 
‘CO30 – Additional capacity of renewable energy production’ adopted by 42 % of the OPs in 8 
Member States, or by 53 % of the Member States that provided data on relevant interventions. 

Table 48. Indicators used by member states in sub-area 1A* 

Shortlisted indicators 

Other indicators 

Member 

State 

CO30 Additional capacity of 

CO34 Estimated 

Reduction of final energy 

Number of SMEs 

renewable energy 

annual decrease of 

consumption in companies (in 

supported with 

production 

GHG 

ktoe) 

consultancies 

BE 

BG 

DE 

DK 

ES 

FR 

HR 

HU 

IT 

LT 

LV 

PL 

PT 

SE 

SI 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

Source: prepared by the study team. 
*Only those indicators which had more than one Member State implementing them were included in the analysis. 

x 

x 

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Most of the 16 Member States that provided data on sub-area 1B.1 adopted common output indicators. Table 
49 shows that the most used common indicators in this area of intervention were:  

• 

• 

• 

• 

‘CO31 – Households with improved energy consumption classification’ adopted by all 15 Member 
States in the sample. However, please note that without sufficient data availability on costs (that 
was described earlier) it was not possible to cover all of the said OPs without making some 
assumptions. 
‘CO34 – Estimated annual decrease of GHG’ which was adopted by 11 Member States, or 69 % of 
the Member States that provided data on relevant interventions. 
‘CO32 – Decrease of annual primary energy consumption of public buildings, in kilowatt-hours 
per year’ adopted by 63 % of Member States in the sample. The relatively wide availability of this 
indicator under the sub-area of 1B.1 can be explained by the presence of public housing initiatives. 
For the purpose of the current analysis, we included such initiatives under sub-area 1B.2. 
‘CO30 – Additional capacity of renewable energy production’ adopted by 7 Member States or by 
44 % of the collected sample.  

Table 49. Indicators used by member states in sub-area 1B.1 

Shortlisted indicators 

Other indicators 

Member State 

CO31 Households with 
improved energy 
consumption classification 

CO34 Estimated 
annual decrease of 
GHG 

CO32 Decrease of annual 
primary energy 
consumption, in kWh/year 

CO30 Additional 
capacity of renewable 
energy production in 
MW 

BE 

BG 

CY 

CZ 

FR 

GR 

HR 

HU 

IE 

LT 

LU 

LV 

MT 

PL 

PT 

RO 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

Source: prepared by the study team. 
*Only those indicators which had more than one Member State implementing them were included in the analysis. 

Lastly,  from  15  Member  States  that  provided  data  on  sub-area  1B.2  operations,  the  most  used  common 
indicators in this area of intervention were: 

• 

• 

‘CO32 – Decrease of annual primary energy consumption of public buildings, in kilowatt-hours 
per year’ adopted by 93 % of the Member States in the sample  
‘CO34 – Estimated annual decrease of GHG’ adopted by 12 Member States or by 80 % of Member 
States in the sample 

157 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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• 

• 

‘CO31 – Households with improved energy consumption classification’ adopted by 47 % of the 
Member States in the sample. The relatively wide availability of this indicator under the sub-area 
can be explained by the presence of public housing initiatives, as was the case in the previous 
section on indicator CO32. For the purpose of the current analysis, we included such initiatives 
under sub area 1B.1 
‘CO30 – Additional capacity of renewable energy production’ adopted by 7 Member States or 47 % 
of the Member States in the sample.  

Table 50. Indicators used by Member States in sub-area 1B.2 

Shortlisted indicators 

Other indicators 

Member 

State 

CO32 Decrease of annual 

primary energy consumption of 

public buildings, in kilowatt-

hours per year 

CO34 Estimated 

annual decrease 

of GHG 

CO31 Number of households 

CO30 Additional 

with improved energy 

capacity of renewable 

consumption classification, in 

energy production in 

units 

MW 

SK 

BE 

BG 

DE 

ES 

FR 

HR 

HU 

IT 

LU 

LV 

MT 

PL 

PT 

RO 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

Source: prepared by the study team. 
*Only those indicators which had more than one Member State implementing them were included in the analysis. 

In addition, other programme-specific indicators have been used in select cases. The table below indicates the 
unique indicators (i.e. only used by a single Member State) which were implemented in the calls / projects in 
the dataset. 

158 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Table 51. Specific indicators used by Member States across all sub-areas 

SMEs (1A) 

Housing sector (1B.1) 

Public sector (1B.2) 

-  Number of enterprises 
receiving support 
-  Private investment 

matching public support 
to enterprises (grants) 
-  Number of consultancy 
sessions arranged by 
established competence 
centres 

-  Number of SMEs 

- 

supported with energy 
audits 
Installation of production 
systems from renewable 
sources 

-  Rate of reduction of 

annual consumption of 
primary energy in 
companies (in %) 

-  Annual reduction of greenhouse gas 

emissions, in kWh/year   

-  Number of users of sustainable urban mobility infrastructure  
-  Heating energy consumption per area in state properties – school building in 

-  Amount of energy produced from 

kWh/sqm 

renewable energy sources (GJ/year)  
-  Annual reduction in primary energy 
consumption of residential buildings 
(kWh/year)  

-  Average consumption of heat energy 

for heating in multi-apartment 
residential buildings after 
implementation of energy efficiency 
improvement measures kWh/m2/year 
-  Number of solid fuel boilers / stoves / 
heating devices replaced as a result of 
implementation of the programme 
-  Number of prepared energy audits 

(pcs.) 

-  Number of modernised heat sources 

-  Energy renovated area m² 
-  Usable area of buildings subjected to thermal modernisation [m2] 
-  Additional electricity generation capacity from renewable sources [MWe]  
-  Additional heat generation capacity from renewable sources [MWt]  
-  Reduction of final energy consumption as a result of project implementation 

[GJ/year]  

-  The amount of heat energy saved [GJ/year]  
-  The amount of electricity saved [MWh/year]  
-  Number of energy-modernised buildings  
-  Number of modernised heat sources  
-  The amount of heat energy saved [GJ/year]  
-  The amount of electricity saved [MWh/year]  
-  Usable area of buildings subjected to thermal modernisation 
-  Number of energy-modernised buildings [pcs.]  
-  Reduction of final energy consumption as a result of project implementation 

[pcs.] 

[GJ/year]  

-  Reduction of final energy 

-  Usable area of buildings subjected to 

consumption in companies 
(in toe) 

thermal modernisation [m2]  
-  Number of energy-modernised 

-  Surface of the building M2 
-  Primary energy consumption in central administration buildings within the 

scope of the operation 

-  Share of renewable energy 

buildings [pcs.]  

-  Area of thermal insulation applied to the opaque envelope of supported 

in gross final energy 
consumption in 
manufacturing industries 
(in Kwh) 

-  Energy savings in 

manufacturing industries 
(in Kwh) 

-  The amount of heat energy saved 

buildings (m2) 

[GJ/year]  

-  The amount of electricity saved  
-  Reduction of final energy 

consumption as a result of project 
implementation [GJ/year]  

-  Area of efficient windows installed in supported buildings (m2) 
-  Decreased power installed in lighting, indoors and outdoors in supported 

buildings (kW) 

-  Estimated contribution to the savings of the average annual required heat 

energy for heating/cooling kWh/m2  

159 

 
 
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SMEs (1A) 

Housing sector (1B.1) 

Public sector (1B.2) 

-  Result Indicator: PV Connected 
Capacity (national indicator)   
Improvement of energy classes 

- 

-  Average energy required for heating / cooling in renovated public sector 

buildings kWh/m2  

-  Contribution to the increase in the use of renewable energy sources (number)   
-  Number of measures of renewable energy sources after and before the energy 

renovation of the building) 

-  Contribution to reducing energy consumption – kWh  
-  Ra4C1 – Number of public buildings having increased their energy efficiency  
-  Ra4c2 – Area rehabilitated public buildings 
-  Area of thermal solar panels installed to produce domestic hot water (DHW) in 

supported buildings (m2) 

-  Number of public buildings with improved energy classification 

-  Reduction of final energy 

consumption in companies 
(in Mwh) 

-  Number of beneficiaries 
-  Reduction of final energy 
consumption as a result of 
project implementation (in 
GJ) 

-  Amount of electricity 
saved (in Mwh) 

-  Production of energy from 

RES (in Mwh) 

-  Number of supported RES 

energy measures 

Source: prepared by the study team. 

160 

 
 
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Data from alternative sources 

In  this  section  of  the  report,  we  take a  more  detailed look  at  the  process  of  preparing  data  from  alternative 
sources  and  the  availability  of  key  data  points  used  to  develop  SCOs  in  the  area  of  energy  efficiency  and 
renewable resources. 

As indicated earlier, we utilised data on  total costs and results achieved in projects contributing  to relevant 
common ERDF/CF indicators which is collected in the study  ‘Monitoring data on ERDF and Cohesion Fund 
operations,  and  on 
the  2014-2020  period’,  contract  N° 
2019CE16BAT214/2020CE16BAT075). The  primary goal of using these data were to calculate SCO values for 
Member States that did not provide the requested historical data (i.e. failed to send it on time and/or provided 
insufficient / poor-quality data). 

the  monitoring  systems  operated 

in 

Data cleaning and filtering 

The indicators which were shortlisted in the analysis were the following:  

•  CO30, defined as the additional capacity of renewable energy production, measured in megawatts 

(MW); 

•  CO31, defined as the number of households with improved energy consumption classification, 

measured in household units; 

•  CO32, defined as decrease of annual primary energy consumption of public buildings, measured 

in kilowatt-hours per year (kWh/year); 

•  CO34, defined as estimated annual decrease of Greenhouse Gas (GHG), measured in tons of CO2 

equivalent. 

Several steps were taken to clean and prepare alternative source data for further analysis:  

•  Removing observations66 with missing data points needed for calculations or filtering of data. 
•  Establishing criteria for shortlisted filtering variables for the dataset. This was done to prepare 
accurate samples for further calculations. The specific criteria chosen for filtering of the microdata 
is presented under each sub-area. 

In order to get a dataset which is complementary to the historical one, irrelevant data from the study Monitoring 
data on ERDF and Cohesion Fund operations, and on the monitoring systems operated in the 2014-2020 period 
were filtered out.  

As far as energy efficiency and renewable energy projects in SME are concerned, the project types considered 
are:    

•  production and distribution of energy derived from renewable sources, 5 637 projects detected; 
• 
energy efficiency and use of renewable energy in business production processes, 3 558 projects 
detected; 
energy efficiency renovation in enterprise buildings, 2 537 projects detected 
support to SME Competitiveness, 1 project detected. 

• 
• 

66 In statistics, an observation is simply one occurrence of something being measured. 

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This  resulted  in  a  preliminary  sample  made  up  of  11 733  projects.  Then,  only  non-repayable  grants  form  of 
financing was considered, leading to a sample of 11 653 units. Finally, a selection was made based on project 
status,  with  only  completed  projects  being  considered.  The  final  sample  size  for  energy  efficiency  and 
renewable energy projects is 8 360. 

As far as energy efficiency and renewable energy projects in housing are concerned, the project types considered 
were:    

• 
community-led development, with 32 projects detected; 
• 
energy efficiency renovation in the housing sector, with 16 876 projects detected; 
•  management and mitigation of climate change consequences, with 3 projects detected; 
•  urban regeneration, with 164 projects detected. 

This  resulted  in  a  preliminary  sample  made  up  of  17 075  projects.  Then,  only  non-repayable  grants  form  of 
financing was considered, leading to a sample of 14 925 units. Finally, a selection was made based on project 
status,  with  only  completed  projects  being  considered.  The  final  sample  size  for  energy  efficiency  and 
renewable energy projects is 11 503. 

The  project  types  selected  for  energy  efficiency  and  renewable  energy  projects  in  public  /  non-residential 
buildings were:  

• 

• 

energy efficiency, smart energy management and renewable energy use in public buildings, with 
17 024 projects detected; 
infrastructure for primary and secondary education, with 2 projects detected. 

This  resulted  in  a  preliminary  sample  made  up  of  17 026  projects.  Then,  only  non-repayable  grants  form  of 
financing were considered, leading to a sample of 16 816 units. Finally, a selection was made based on project 
status,  with  only  completed  projects  being  considered.  The  final  sample  size  for  energy  efficiency  and 
renewable energy projects was 9 470.  

Table 52. Overview of data from alternative sources 

MEMBER 
STATE 

EE-RES SMES 

EE-RES HOUSING 

EE-RES NON-RESIDENTIAL 

No. of OPs 

No. of 
projects 

No. of OPs 

No. of 
projects 

No. of OPs 

No. of 
projects 

AT 

BE 

BG 

CY 

CZ 

DE 

DK 

EE 

ES 

FI 

FR 

N/A 

N/A 

N/A 

N/A 

1 

10 

N/A 

N/A 

12 

N/A 

23 

N/A 

N/A 

N/A 

N/A 

27 

672 

N/A 

N/A 

2878 

N/A 

343 

N/A 

1 

N/A 

N/A 

2 

N/A 

N/A 

1 

12 

N/A 

22 

N/A 

4 

N/A 

N/A 

2187 

N/A 

N/A 

417 

2190 

N/A 

618 

N/A 

N/A 

N/A 

N/A 

2 

9 

N/A 

1 

15 

N/A 

20 

N/A 

N/A 

N/A 

N/A 

1177 

476 

N/A 

127 

1400 

N/A 

342 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

GR 

HR 

HU 

IE 

IT 

LT 

LU 

LV 

MT 

NL 

PL 

PT 

RO 

SE 

SI 

SK 

N/A 

N/A 

N/A 

N/A 

9 

1 

N/A 

1 

N/A 

N/A 

15 

N/A 

N/A 

N/A 

N/A 

N/A 

N/A 

N/A 

N/A 

N/A 

748 

43 

N/A 

97 

N/A 

N/A 

1287 

N/A 

N/A 

N/A 

N/A 

N/A 

N/A 

1 

N/A 

1 

2 

N/A 

N/A 

N/A 

N/A 

N/A 

11 

N/A 

N/A 

1 

N/A 

N/A 

N/A 

527 

N/A 

6 

6 

N/A 

N/A 

N/A 

N/A 

N/A 

261 

N/A 

N/A 

1 

N/A 

N/A 

1 

1 

N/A 

N/A 

18 

N/A 

1 

1 

1 

N/A 

16 

N/A 

N/A 

3 

N/A 

N/A 

21 

483 

N/A 

N/A 

575 

N/A 

1 

175 

1 

N/A 

1379 

N/A 

N/A 

14 

N/A 

N/A 

Source: prepared by the study team. 

Data points 

The  dataset  provided  us  with  detailed  information  on  projects  which  cover  our  four  common  ERDF/CF 
indicators which were chosen as potential indicators that could be developed into EU-level SCOs. The following 
data points are key to facilitate SCO calculations: 

• 
• 

implemented total expenditures, in EUR; 
implemented total number outputs, in units (based on common ERDF/CF indicators). 

Table 53 below indicates the availability of these data points within the analysed dataset. 

Table 53. Availability of key data points needed for SCO development 

MEMBER 
STATE 

AT 

BE 

BG 

CY 

CZ 

DE 

DK 

EE 

ES 

EE-RES SMES 

EE-RES HOUSING 

EE-RES NON-RESIDENTIAL 

CO30 

CO34 

CO31 

CO34 

CO32 

CO34 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

FI 

FR 

GR 

HR 

HU 

IE 

IT 

LT 

LU 

LV 

MT 

NL 

PL 

PT 

RO 

SE 

SI 

SK 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

Source: prepared by the study team. 

The dataset also contained other columns with further relevant details, such as project start / end dates, thematic 
objectives and typologies of the  covered projects. There  were also project descriptions, which  gave us some 
qualitative  details  on  what  types  of  activities  were  covered  in  analysed  projects,  albeit  the  quality  of  these 
descriptions was rather poor. 

Caveats 

The dataset lacked detailed information on costs – aside from data on total costs, there was no indication on 
what was being covered in implemented projects. It also lacked structured information on supported activities 
– only general project descriptions were available, and their completeness / level of detail varied considerably. 
Moreover, at times this source provided incomplete data as some of the projects in the dataset have missing 
data points for total implemented expenditure and/or number of implemented outputs. These values could not 
be used in the analysis, at least for calculating the considered unit cost. Data availability was higher for allocated 
expenditure and/or planned number of outputs. However, these data are not verifiable and relies on forecasting 
rather than actual results of the project. 

Overall, the study team was not able to rely on this source to estimate the cost of each activity included in the 
definition of proposed SCOs. Nevertheless, it can be exploited to compensate for gaps in the collected historical 
data and ensure a better coverage of countries if/when calculating SCOs-based total costs of the operation. 

164 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Research and Innovation activities  

Data collected from Member States 

After  receiving  the  populated  DCFs,  we  compiled  the  historical  data  collected  for  all  three  sub-areas  into  a 
single dataset. An integral part of this process was assessing if the quality and level of detail of these data were 
sufficient for developing EU-level SCOs. It also involved a preliminary cleaning of the data to identify any gaps 
(i.e. the missing values) and, if possible, address any evident cases of irrelevant or poor-quality data.  

Our team used the following set of criteria for assessing the collected data: 1) data availability, 2) data quality, 
and 3) data granularity. Each criterion is further specified in Table 54. 

Table 54. Criteria used for assessing the collected historical data 

CRITERIA 

Data 
availability 

DESCRIPTION 

Relates  to  the  collected  data  sample,  its  completeness  and  comprehensiveness.  The  scope  and 
representativeness of data received from the Member States in terms of the number of areas and projects 
are  analysed  during  this  step.  This  presumes  that  all  data  points  needed  for  calculations  and 
establishment of a particular SCO are sufficiently covered in the data sample, whereas the latter provides 
reasonable coverage of Member States. Incomplete data (i.e. provisional /  estimated data or data gaps) 
are as dangerous as inaccurate data. Gaps in data lead to a partial view of the overall picture. Without a 
complete picture of how projects are funded, SCOs may be calculated through uninformed actions. 

Data quality   Relates to accuracy, consistency, timeliness, and comparability. This relates to the clarity of the key data 
points in the data collection form, especially the costs of facilitating relevant activities, information on 
eligible cost categories and the number of outputs. The collected data cannot contradict a value residing 
in a different source or collected by a different system. The data must be logical, without contradiction or 
unwarranted variance. This criterion also involves checking whether the supplied data fit the established 
reference period and other data for that Member State. 

Data 
granularity 

Relates  to  the  level  of  detail  at  which  data  are  collected.  This  is  important  because  confusion  and 
inaccurate decisions can otherwise occur. Aggregated, summarised, and manipulated collections of data 
could offer a different meaning than the data implied at a lower level. An appropriate level of granularity 
must be defined to provide sufficient uniqueness and for distinctive properties to become visible. This is 
a requirement for the development of accurate SCOs. 

Source: prepared by the study team. 

As  far  as  the  availability  of  the  collected  data  are  concerned,  Table  55  offers  a  complete  overview  of  the 
information gathered at the level of the three sub-areas analysed in this section. It indicates the number of OPs 
and projects / calls for proposals for which relevant data were provided by each Member State: 

• 

• 

• 

Sub-area 2A (Knowledge transfer of SMEs): data on 18 OPs from 13 Member States were made 
available to the study team, which amounts to 133 records on specific projects or calls for 
proposals; 
Sub-area 2B (Innovation vouchers for SMEs): data on 17 OPs from 12 Member States were 
collected, which amounts to 2 252 records referring to either specific projects or calls for proposals; 
Sub-area 2C (RDI research projects): data on 50 OPs were provided by 25 Member States, which 
amounts to 3 815 records on specific projects or calls for proposals. 

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Table 55. Overview of data coverage per sub-area 

MEMBER 
STATE 

AT 

BE 

BG 

CY 

CZ 

DE 

DK 

EE 

ES 

FI 

FR 

GR 

HR 

HU 

IE 

IT 

LT 

LU 

LV 

MT 

NL 

PL 

PT 

RO 

SE 

SI 

SK 

KNOWLEDGE TRANSFER 

INNOVATION VOUCHERS 

RDI RESEARCH PROJECTS  

OF SMES (2A) 

FOR SMES (2B) 

(2C) 

N. OP 

N. PROJ./ 

CALL 

N. OP 

N.PROJ/ 

CALL 

N. OP 

 1 

2 

1 

1 

3 

1 

2 

1 

1 

1 

2 

1 

1 

1 

2 

3  

1 

35 

20 

8 

2 

 14 

2 

6 

36 

 2 

2 

1 

1 

1  

1 

1 

1 

1 

 2 

1 

5 

1 

1 

1 

3 

29  

1  

33 

571 

1 

 17 

1 

23 

1 571 

1  

2 

1 

1 

2 

6 

1 

1 

1 

1 

6 

2 

1 

2 

1 

4 

1 

1 

1 

9 

1 

1 

1 

1 

1 

N.PROJ/ 

CALL 

 13 

2 

16 

 13 

15 

1 178 

 23 

3  

42 

1 558  

247 

7  

293 

13 

2  

33 

3 

2 

27  

187 

89 

12 

3  

3  

31 

Source: prepared by the study team. 

Quality-wise,  we  checked  if  all  key  data  points  necessary  for  the  SCO  development  were  provided  in  the 
requested format, considering activities, costs and outputs / results.  

Total  costs  were  available  for  the  vast  majority  of  the  cases  in  sub-area  2A,  missing  only  1  out  of  133  cases 
(0.75 %), for 38 out of 2 252 cases in sub-area 2B (1.68 %) and for 160 out of 3 815 cases in sub-area 2C (4.19 %). 
The activities supported were often described by data providers, even if, in some cases, they were  not well-
detailed. In fact, the activities were often not accompanied by the categories of costs covered under the specific 
activities, so data providers were, in most cases,  unable  to specify the  breakdown of amounts spent in  each 
category. The lack of breakdown was generally explained by the fact that some Member States do not collect 
data at such level of detail. Moreover, in some cases, instead of directly mentioning the supported activities, 
data providers referred to the related calls for proposals.  

166 

 
 
  
  
  
 
 
 
 
 
 
  
  
  
  
 
 
  
  
 
 
  
  
  
  
  
  
  
  
 
 
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
  
  
 
 
 
 
  
  
  
  
  
  
Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

Another important limitation of these data were the inconsistency or variety of supported activities, as well as 
of output / results indicators, across analysed Member States, even within the same sub-area. Collected data in 
many cases were insufficiently granular and therefore only partially comparable. Building on this observation, 
we compiled an overview of supported activities for each Member State, facilitating a comparison of the scope 
of activities across countries and selection of the most relevant activities for each of the three sub-areas.  

Most of the historical data were provided at the level of individual projects: 59.4 % of records for sub-area 2A, 
96.4 % for sub-area 2B, and 87.1 % for sub-area 2C are available at this level of granularity. Only 15 % of records 
were available at the level of calls for proposals in the case of sub-area 2A, for 0.2 % of records in sub-area 2B 
and  3.8 %  of  records  in  sub-area  2C.  For  the  remaining  records  in  sub-area  2A,  2B,  and  2C,  the  level  of 
granularity  could  not  be  clearly  identified  because  either  it  was  not  specified  or  the  data  were  provided  at 
various levels of detail (25.6 %, 3.4 %, 9.1 %, respectively). 

Only a small portion of the cost data provided to the study relates to ongoing projects: 14.3 % in sub-area 2A, 
2.5 % in sub-area 2B, and 23.6 % in sub-area 2C, while 82 % of records in sub-area 2A, 97.3 % in sub-area 2B, 
and 75.6 % in sub-area 2C refer to completed projects. In 3.8 % of available records for sub-area 2A, 0.2 % of 
sub-areas 2B and 0.8 % of sub-area 2C, it was not possible to clearly define the progress level of selected projects 
due to unclear or incomplete information. 

Most of the projects / calls for proposals were implemented during the period 2018-2019 and in the last 3 years, 
i.e. in the period 2019-2021. In some cases, however, the reference period was unspecified – the historical data 
in question refer to the entire programming period of 2014-2020, especially for sub-area 2B and 2C. 

As far as the eligibility rules are concerned, 33.1 % of records in sub-area 2A were missing, while there was no 
specific  eligibility  rule  for  3.8 %  of  records  in  the  same  area.  Regarding  sub-area  2B,  for  2.6 %  of  records, 
eligibility rules were missing. Finally, 6.4 % of records for eligibility rules in sub-area 2C were missing, while 
for 1.5% of records there was no specific eligibility rule. 

Typologies of activities according to collected data 

Based on the analysis of the qualitative information gathered in sub-area 2A, no Member State implemented 
the  full  set  of  activities  identified  in  the  DCF.  However,  compared  to  France,  Hungary,  Italy  or  Romania, 
countries such as Belgium, Latvia or Slovenia, funded a significantly narrower subset of activities. Table 56 lists 
the main activities funded by Member States. The most common / most frequently overlapping activities were 
the following:  

• 

• 

support measures for creating spin-offs to increase the entrepreneurial capacity of research 
organisations and promote the translation of research results into economic value; 
support for industrial PhDs and traineeships. 

Table 56. Main typologies of activities supported in sub-area 2A* 

ACTIVITY 

Activities to 
stimulate 
knowledge 
transfer 

BE 

CZ 

DE 

FR 

HU 

IT 

LV 

LU 

PT 

RO 

MEMBER STATE 

x 

x 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

ACTIVITY 

Post-doctoral 
researcher 
placements in the 
private sector, 
including SMEs 

Project 
management 

Project 
preparation 

Research on new 
building materials 

Specialist 
consulting and 
external services 
Support for 
industrial PhDs 
and traineeships 
Support measures 
for creating spin-
offs to increase 
the 
entrepreneurial 
capacity of 
research 
organisations and 
promote the 
translation of 
research results 
into economic 
value 

Tools and 
equipment 

BE 

CZ 

DE 

FR 

HU 

IT 

LV 

LU 

PT 

RO 

MEMBER STATE 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

Source: prepared by the study team. 
*Only those activities funded by more than one Member State have been included in the table.  

In  sub-area  2B,  no  Member  State  funded  all  activities  identified  in  the  DCF.  Estonia,  Hungary,  Italy  and 
Slovenia, implemented projects where only a small subset of these activities was funded. Other Member States 
covered  a  wider  range  of  activities  (such  as  France,  Cyprus,  and  Czechia).  Table  57  lists  the  main  funded 
activities. 

Despite observed variation in typologies, a significant number of mapped activities seemed to overlap at least 
to  some  degree  in  projects  implemented  in  the  analysed  Member  States.  The  most  frequently  overlapping 
activities were as follows:  

• 

consultancy services provided to SMEs by universities, research centres or knowledge-intensive 
companies; 

•  development of digital capabilities; 
• 

establishing and sustaining contacts between SMEs and research facilities, nationally or 
internationally. 

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Table 57. Main typologies of activities supported in sub-area 2B* 

ACTIVITY 

CY 

CZ  DK 

EE 

FR 

HU  HR 

IT  MT 

PL 

PT 

SI 

MEMBER STATE 

Access to research centres and facilities 

Consultancy services provided to SMEs by 
universities, research centres or 
knowledge-intensive companies 

Development of digital capabilities 

Establishing and sustaining contacts 
between SMEs and research facilities, 
nationally or internationally  

Investment in ICT instruments 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

Source: prepared by the study team. 
*Only those activities funded by more than one Member State have been included in the table.  

In sub-area 2C, the analysis of the activities was performed through the use of keywords, as there was a large 
number  of  different  activities.  The  analysis  revealed  that  many  activities  included  in  the  DCF  were 
implemented  by  Germany,  Romania,  Poland,  Hungary  and  Bulgaria.  A  few  other  Member  States  funded  a 
slightly  smaller  subset  of  activities,  such  as  the  case  of  the  Latvia,  the  Netherlands,  and  Slovakia,  that 
implemented  much  smaller  projects  where  only  one  or  two  activities  were  funded.  Table  58  lists  the  main 
activities  funded  by  Member  States.  Overall,  based  on  the  available  data,  the  most  common  overlapping 
activities in sub-area 2C were: 

• 
• 
• 
• 
• 

industrial research; 
experimental development; 
testing activities; 
conceptual / designing / development studies; 
infrastructural investments.

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Table 58. Main typologies of activities supported in sub-area 2C* 

ACTIVITY 

Activities for obtaining, 
validating and protecting 
property rights 

Conceptual/designing/developm
ent studies 

Construction activities 

Consultancy services 

Organising and holding 
conferences, seminars and round 
tables 
Development of new products 
and solutions in the 
collaboration between 
enterprises and public research 
institutions 

T
A

E
B

G
B

Y
C

Z
C

E
D

K
D

E
E

S
E

I
F

R
F

R
G

R
H

U
H

E
I

T
I

V
L

T
L

T
M

L
N

L
P

T
P

MEMBER STATE 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

Experimental development 

x 

x 

Feasibility study 

Fundamental research  

Industrial research 

Infrastructural investments 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

O
R

x 

x 

x 

x 

x 

x 

x 

E
S

I
S

K
S

x 

x 

x 

x 

170 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

ACTIVITY 

Project management 

Testing activities 

T
A

E
B

G
B

Y
C

Z
C

E
D

K
D

E
E

S
E

I
F

R
F

R
G

R
H

x 

x 

x 

x 

x 

x 

U
H

x 

E
I

T
I

V
L

T
L

T
M

L
N

L
P

T
P

O
R

E
S

I
S

K
S

x 

x 

x 

x 

x 

x 

x 

MEMBER STATE 

Source: prepared by the study team. 
*Only those activities funded by more than one Member State have been included in the table.  

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Costs incurred according to collected data 

This  section  offers  an  overview  of  the  analysis  of  the  categories  for  which  data  on  the  total  cost 
breakdown at category / item level were provided.  

On the basis of the available data, it was possible to make an initial analysis of the composition of these 
operations among the different Member States. For instance, it was observed that the different Member 
States were characterised by a certain degree of heterogeneity in terms of the costs incurred. For this 
reason, standardisations of cost categories received for each sub-area have been performed to facilitate 
the process of computation. In particular, it was necessary to create common cost categories to derive 
the shares of each cost category by detected outputs. 

Concerning  sub-area  2A, different  cost  categories  have  been  merged  according  to  the  types  of  costs 
described under the Marie Skłodowska-Curie Actions (MSCA) Staff Exchanges (i.e. staff costs / research 
training, networking costs / management and indirect costs) and ToR (i.e. staff costs for researchers / 
allowances / research costs: experimental research, non-experimental research, overheads, research / 
training costs). The macro-categories identified were:  

staff costs 
research training and networking costs 

• 
• 
•  management and indirect costs 
• 
experimental research costs 
•  overhead costs. 

The specific categories that were merged are detailed in Table 59. 

Table 59. Macro-categories in sub-area 2A 

MACRO-CATEGORY 

SPECIFIC CATEGORIES 

Staff costs 

Research training and 
networking costs 

Management and 
indirect costs 

Experimental research 
costs 

• 
• 

• 
• 

• 
• 
• 
• 
• 
• 
• 
• 
• 

• 
• 
• 

• 

Personnel costs 
Salary  expenses  for  industrial  research,  related  to  the  personnel  involved  in  the 
implementation of the project (in the course of activities, other than project management) 
Salary expenses with the project management team 
Salary  expenses  with  the  staff  involved  in  the  implementation  of  the  project  (in  the 
course of activities, other than project management)   
Salary for collection staff 
Staff costs of researchers 
Cost of allowances for networking, mobility and other activities  
Cost of training and coaching costs for professional support for researchers 
Expenditure on services for the organisation of events and training courses 
Expenditure on the purchase of inventory and consumables for the management team 
Indirect costs 
Project management 
Project  management  activities  (including  project  information  and  publicity  activities). 
Type E activity 
SCO of 15 % of the total costs to cover the project management costs 
Travel expenses for project management staff 
Expenditure  on  experimental  development  related  to  consultancy  and  equivalent 
services 
Expenditure on the acquisition of tangible fixed assets (other than land and buildings) 
for experimental development  

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MACRO-CATEGORY 

SPECIFIC CATEGORIES 

• 

• 
• 

• 

Expenses  for  experimental  development  with  the  purchase  of  raw  materials, 
consumables and other similar products necessary for the project 
Experimental development 
Industrial research and/or experimental development activities carried out in effective 
collaboration with an enterprise.  
Salary  expenses  for  experimental  development,  related  to  the  staff  involved  in  the 
implementation of the project (in the course of activities, other than project management) 
Cost of overheads 

• 
•  Overheads (20 % flat rate) 
•  General administrative expenses 

Overhead costs 

Source: prepared by the study team. 

As far as sub-area 2B is concerned, a small number of cost categories were identified, compared to the 
other sub-areas. Nonetheless, cost categories were merged. The macro-categories identified were:  

equipment and materials costs 
intangible assets costs 
ICT instruments costs 
strategic innovation costs 

▪ 
▪ 
▪ 
▪ 
▪  publicity costs 
▪ 

consulting costs 

The specific categories merged are detailed in Table 60. 

Table 60. Macro-categories in sub-area 2B 

MACRO-CATEGORY 

SPECIFIC CATEGORIES 

Equipment and 

materials costs 

Intangible assets costs 

▪ 

▪ 

▪ 

▪ 

Costs of materials 

Costs of working equipment 

Expertise and external services: costs related to obtaining, validating and defending 

patents and other intangible assets 

Purchase of intangible assets 

▪  Hosting, domain and other licences 

ICT instruments costs 

▪ 

Secure services online payment 

▪  Website design and development 

Strategic innovation 

costs 

▪  Assistance in intellectual property management total service cost of finished projects 

costs of contract research, research services 

Business innovation processes + corporate innovation processes (with application to 

the certifying body) 

Business model innovation 

Cost of the service consisting in the development of a new or significantly improved 

product, service, production technology or a new design project 

Cross-financing, grants, information and promotion, consultancy services, intangible 

services, non-consultancy services, operating costs 

Innovation costs (innovation in the concept phase + innovation in the design and 

testing phases)  

strategic innovation for the introduction of new products / services 

▪ 

▪ 

▪ 

▪ 

▪ 

▪ 

▪  Organisational upgrading + organisational upgrading (with application for ISO 

9001/2015 and SMI certification) 

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MACRO-CATEGORY 

SPECIFIC CATEGORIES 

Publicity costs 

Consulting costs 

▪ 

▪ 

▪ 

▪ 

▪ 

▪ 

Staff costs 

Strategic planning 

Expertise and external services: procurement of support / advisory services 

Promotional planning   

Expertise and external services: procurement of innovation support services 

Specialist support 

Source: prepared by the study team. 

Finally,  in  sub-area  2C  the  heterogeneity  of  costs  categories  detected  made  their  standardisation 
necessary. In particular, the macro-categories identified were:  

staff costs 
travel costs 
communication costs 
scientific, technical equipment and materials costs 

▪ 
▪ 
▪ 
▪ 
▪  overhead costs 
▪ 
▪ 
▪  patent costs 
▪  project management costs 

external services 
indirect costs 

The specific categories merged are listed in Table 61. 

Table 61. Macro-categories in sub-area 2C 

MACRO-

CATEGORY 

Staff costs 

SPECIFIC CATEGORIES 

▪ 

20 % personnel costs           

▪  Direct cost of personnel             

▪ 

▪ 

▪ 

▪ 

▪ 

▪ 

▪ 

▪ 

▪ 

▪ 

Expenditure on the secondment of highly qualified staff         

Indirect cost in the form of simplified costs (20 % of staff costs)             

Indirect costs (20 % staff costs)         

Personnel costs     

Personnel costs (employment contracts, civil law contracts)             

Salaries and charges 

Salary costs 

Staff costs   

Staff costs – remuneration with taxes and employees’ social security contributions     

Staff costs of researchers, technical and other staff             

Travel costs 

▪  Mission and travel cost 

▪ 

▪ 

▪ 

▪ 

▪ 

▪ 

▪ 

▪ 

Travel and reception costs 

Travel costs 

Travel costs of researchers 

Travel expenses 

Travel, catering and accommodation expenses 

Communication 

Communication animation events 

Communication tools 

Communication 

costs 

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MACRO-

CATEGORY 

SPECIFIC CATEGORIES 

Scientific, technical 

equipment and 

materials costs 

Overhead costs 

External services 

costs 

Indirect costs 

Patent costs 

▪ 

▪ 

▪ 

▪ 

▪ 

▪ 

▪ 

▪ 

▪ 

▪ 

▪ 

▪ 

Cost of communication 

Cost of using scientific equipment 

Costs of scientific and technical equipment (costs of apparatus and equipment - depreciation 

write-offs) 

Costs of scientific and technical equipment (costs of apparatus and equipment - depreciation 

write-offs, rental costs) 

Costs of scientific and technical equipment (costs of apparatus and equipment - depreciation 

write-offs, equipment rental costs, capital instalments for equipment) 

Costs of scientific and technical equipment (depreciation) 

Costs of scientific and technical equipment and materials 

Equipment and fire safety plan 

Equipment costs 

Equipment for premises (signage, blinds, etc.) 

Equipment, plant and machinery 

Internal equipment 

▪  Operating cost (rental of laboratory space, purchase of laboratory equipment, purchase of 

materials, prototype elements) 

▪  Other costs (laboratory consumables, small-scale equipment and configuration of research 

facilities and software purchase A.1.7, A.1.10, A.1.11) 

Small equipment 

Tools and equipment + expenditure on the acquisition of tangible fixed assets (other than 

land and buildings), inventories, raw materials and materials, including consumables (RO)  

Cost of overheads 

▪ 

▪ 

▪ 

▪  General administrative expenses 

▪ 

▪ 

▪ 

▪ 

▪ 

▪ 

▪ 

▪ 

▪ 

▪ 

▪ 

▪ 

▪ 

▪ 

▪ 

▪ 

▪ 

▪ 

▪ 

▪ 

▪ 

Costs for external contributions 

Costs for services 

Expenditure on external services 

Expenditure on services 

Expert and legal support (analysis, expert opinions / advice) 

External services necessary for the proper functioning of the actions 

Purchases and services 

Service and maintenance expenses 

Specialist consulting and external services 

Indirect cost (20 % of payroll) 

Indirect costs 

Indirect costs of operating 

Indirect expenditure under Article 68 (1) (b) 

Costs for obtaining, validating and defending patents and other intangible assets 

Costs for obtaining, validating and defending patents and other intangible assets and costs 

for innovation advisory and support services 

Costs of contractual research, knowledge and patents 

Costs of obtaining and validating patents 

Costs of patents, licences, etc. 

Intangible assets 

Knowledge and patents costs 

Expenditure for obtaining, validating and protecting patents and other intangible assets 

Project management 

▪  Management fees 

costs 

▪ 

Project management costs 

175 

 
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Source: prepared by the study team. 

Concerning the availability of cost breakdowns at the activity level, in general, detailed information 
was  limited  across  all  sub-areas  of  Area  2.  While  several  Member  States  have  been  able  to  provide 
information on total costs incurred, not all of them were able to provide complete information in terms 
of financial amounts for each activity funded.  

Out of the Member States that provided data for the sub-areas, 11 Member States were able to provide 
data in the required detail in sub-area 2A, 7 in sub-area 2B and 15 in 2C, as indicated in Table 62. It is 
worth noting that the fact that not all MS that provided data at the activity level did so for all activities 
under the three sub-areas, it means that few complete sets of data are available. In turn, this means that 
we had to rely on statistical techniques to estimate the values for those Member States which possess 
partial data. 

Table 62. Availability of breakdown of costs at the activity level 

MS 

AT 

BE 

BG 

CY 

CZ 

DE 

DK 

EE 

ES 

FI 

FR 

GR 

HR 

HU 

IE 

IT 

LT 

LU 

LV 

MT 

NL 

PL 

PT 

RO 

SE 

KNOWLEDGE TRANSFER OF 
SMES (2A) 

INNOVATION VOUCHERS FOR 
SMES (2B) 

RDI RESEARCH PROJECTS 
(2C) 

YES 

NO – ONLY TOT COSTS 

NO – ONLY TOT COSTS 

YES 

YES 

YES 

NO – ONLY TOT COSTS 

NO – ONLY TOT COSTS 

NO – ONLY TOT COSTS 

YES 

NO – ONLY TOT COSTS 

NO – ONLY TOT COSTS 

NO – ONLY TOT COSTS 

NO – ONLY TOT COSTS 

NO – ONLY TOT COSTS 

YES 

YES 

NO – ONLY TOT COSTS 

NO – ONLY TOT COSTS 

YES 

YES 

NO – ONLY TOT COSTS 

NO – ONLY TOT COSTS 

NO – ONLY TOT COSTS 

YES 

YES 

YES 

YES 

YES 

YES 

YES 

YES 

YES 

NO – ONLY TOT COSTS 

NO – ONLY TOT COSTS 

YES 

YES 

YES 

YES 

YES 

YES 

YES 

YES 

NO – ONLY TOT COSTS 

YES 

YES 

YES 

NO – ONLY TOT COSTS 

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MS 

SI 

SK 

KNOWLEDGE TRANSFER OF 
SMES (2A) 

INNOVATION VOUCHERS FOR 
SMES (2B) 

RDI RESEARCH PROJECTS 
(2C) 

YES 

YES 

YES 

YES 

Source: prepared by the study team. 

Outputs and results produced according to collected data 

From the analysis of outputs and results of the calls / projects for which data were collected, we have 
observed  that  there  was  significant  heterogeneity  among  Member  States  in  the  outputs  being 
monitored. While we were able to identify some common indicators (Table 63) OPs recorded a large 
number of specific measures, which created some challenges for the development of EU-level SCOs in 
Area 2, in particular for sub-area 2C. 

In sub-area 2A, the most used indicators were: 

▪  number of enterprises cooperating with research institutions (CO26), adopted by 9 

Member States; 

▪  number of new researchers in supported entities (CO24), adopted by 7 Member States; 
▪  number of enterprises receiving support (CO01), adopted by 6 Member States. 

In sub-area 2B, the most frequent measures were: 

▪  number of enterprises supported to introduce new to the firm products (CO29), adopted 

by 4 Member States; 

▪  number of enterprises supported to introduce new to the market products (CO28), 

adopted by 4 Member States; 

▪  number of enterprises receiving support (CO01), adopted by 4 Member States; 
▪  number of enterprises cooperating with research institutions (CO26), adopted by 2 

Member States. 

It is  notable  that sub-area  2B collected data for  operations  on  ‘innovation vouchers for  SMEs for 
R&D  implementation’  therefore  some  projects  (denoted  as  rows  in  the  dataset)  were  meant  to 
facilitate one innovation voucher. Overall, the study team has 10 Member States for which we have 
either separate rows for an innovation voucher, or innovation vouchers being tracked as ‘outputs’ 
in a larger scale project. 

Finally, in sub-area 2C, the most common indicators were: 

▪  number of enterprises receiving support (CO01), adopted by 13 Member States; 
▪  number of new researchers in supported entities (CO24), adopted by 12 Member States; 
▪  number of enterprises cooperating with research institutions (CO26), adopted by 9 

Member States. 

In sub-area 2A, outside of the more common indicators listed above, 23 different outputs / results were 
indicated  by  the  OPs.  A  similar  situation  happened  in  sub-area  2B,  with  22  specific  indicators.  As 
mentioned above, the most problematic scenario happened in sub-area 2C, which provided a total of 

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78 unique indicators indicating significant heterogeneity between Member States and within different 
OPs of the Member State. 

Table 63. Historical data provided on most common outputs / results indicators per sub-area 

MEMBER 
STATE 

KNOWLEDGE TRANSFER 
OF SMES (2A) 

INNOVATION VOUCHERS FOR 
SMES (2B) 

RDI RESEARCH PROJECTS 
(2C) 

CO26 

CO24 

CO01 

CO29 

CO28 

CO01 

CO26 

CO26 

CO24 

CO01 

BE 

CY 

CZ 

DE 

DK 

FR 

GR 

HR 

HU 

IT 

LU 

LV 

MT 

PL 

RO 

SE 

SI 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

 x 

x 

x 

x 

x 

Source: prepared by the study team. 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

178 

 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

Data from alternative sources 

The study team has gathered relevant data from public sources and conducted a comprehensive analysis of 
alternative sources in relation to their quality and suitability for SCO development in Area 2.   

Development of one of the alternatives for sub-area 2A relies on existing information from Marie Skłodowska-
Curie Actions (MSCA): Staff exchanges action. Namely, our study made use of the unit costs developed for 
intersectoral mobility of research staff. The indicator information collected from this alternative source acted as 
proxy for standardised information that has not been provided by Member States. The action contributes to 
boosting  jobs,  growth  and  investment  by  training  researchers  for  new  knowledge  and  skills.  It  encourages 
short-term  international,  interdisciplinary  and  inter-sectoral  exchanges  (secondments)  of  staff  members 
involved  in  research  and  innovation  activities  of  participating  organisations.  It  aims  to  develop  sustainable 
collaborative  projects  between  different  organisations  from  the  academic  and  non-academic  sectors  (in 
particular  SMEs),  based  in  Europe  and  beyond.  This  proxy  source  contains  the  following  relevant  aspects 
related  to  intersectoral  mobility  of  researchers,  such  as  staff  costs;  research,  networking,  training  costs  and 
management costs / indirect costs. 

Furthermore, as a complementary source the historical data collected for sub-area 2A, an additional source of 
relevant data were identified, namely the study ‘Monitoring data on ERDF and Cohesion Fund operations, 
and on the monitoring systems operated in the 2014-2020 period’. The study, among other things, collected 
data  on  total  costs  and  results  achieved  in  ERDF/CF  projects,  including  projects  in  the  area  of  research  and 
Innovation.  It  also  collected  relevant  information  on  common  ERDF  indicators,  such  as  CO24  (gross  new 
working position (that did not exist before) to directly perform R&D activities in the SME). 

For the development of SCOs in sub-area 2B we used information from the Community Innovation Survey 
(Eurostat). This biennial data collection provides information on statistics about enterprises that have product 
and business process innovations, their strategies, knowledge management and innovation activities, as well 
as  about  factors  that  facilitate  or  hinder  innovation  ('innovation  environment').  The  core  statistics  cover 
innovation  activities,  cooperation,  development,  expenditures  and  turnover.  The  Community  Innovation 
Survey  provides  statistics  about  enterprises  that  have  product  and  business  process  innovations,  their 
strategies,  knowledge  management  and  innovation  activities.  This  data  source  was  primarily  used  as  a 
triangulation source with historical data. 

Table 64. Relevant variables from Community Innovation Survey 

VARIABLES 

DESCRIPTION 

Filter variables 

Size  of  enterprise  (e.g.  from  10  to  49  employees;  from  50  to  249  employees);  type  of  enterprise  (e.g.  innovative 

enterprises and non-innovative enterprises); type of activities (e.g. Innovation core activities (Com.Reg. 995/2012)). 

Expenditure 

Absolute EUR figures (or % out of all costs) for expenditure on R&D activities performed in-house and contracted 

variables 

out,  capital  goods  for  innovation  (such  as  acquisition  of  machinery,  equipment,  software,  intellectual  property 

rights (IPRs), rent for buildings and others). 

Output/result 

Indicators of created outputs such as new or significantly improved products to the firm or the market; new or 

variables 

improved  processes  related  to  business  process  innovation,  new  or  improved  methods  for  producing  goods  or 

providing services. 

Indicators of hampering factors for innovation activities, such as lack of qualified employees within enterprise, lack 

of collaboration partners or lack of access to external knowledge. 

Source: Metadata for Community Innovation Survey. 

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

SME Growth and Competitiveness  

The assessment of the quality of the historical data collected under this area involves checking the following 
data points due to their necessity for the EU-level SCO development process, namely: 

•  data on costs incurred in implemented projects, possibly broken down by activity and by category 

of costs: these data are important for attribution of incurred costs, determination of a standard set 
of cost items included in the definition of a SCO, identification of outlier cases, elimination of gaps 
in the cost breakdown data, etc.; 
information on activities supported in implemented projects: these data are important for 
comparing the interventions supported by the Member State over time and across different 
Member States; 

• 

•  data on inputs, outputs and results: these data are important for estimation and attribution of the 
monetary value to indicators used when developing SCOs, especially standard scales of unit costs. 

Data collected from Member States 

After receiving the populated DCFs, we compiled the historical data collected for the two sub-areas into a single 
dataset.  An  integral  part  of  this  process  was  assessing  if  the  quality  and  level  of  detail  of  these  data  were 
sufficient for developing EU-level SCOs. It also involved a preliminary cleaning of the data in order to identify 
any gaps (i.e. the missing values) and, if possible, address any evident cases of irrelevant or poor-quality data.  

Our team used the following set of criteria for assessing the collected data: 1) data availability, 2) data quality, 
and 3) data granularity. Each criterion is further specified in Table 65. 

Table 65. Criteria used for assessing the collected historical data 

CRITERIA 

Data 
availability 

DESCRIPTION 

Relates to the collected data sample, its completeness and comprehensiveness. The scope and representativeness of 
data  received  from  the  Member  States  in  terms  of  number  of  areas  and  projects  is  analysed  during  this  step.  This 
presumes that all data points needed for calculations and establishment of a particular SCO are sufficiently covered in 
the data sample, whereas the latter provides a reasonable coverage of Member States. Incomplete data (i.e. provisional 
/ estimated data or data gaps) are as dangerous as inaccurate data. Gaps in data lead to a partial view of the overall 
picture. Without a complete picture of how projects are funded, SCOs may be calculated through uninformed actions. 

Data quality   Relates to the accuracy, consistency, timeliness and comparability. This relates to the clarity of the key data points in 
the  data  collection  form  (DCF),  especially  the  costs  of  facilitating  relevant  activities,  information  on  eligible  cost 
categories and number of outputs. The collected data cannot contradict a value residing in a different source or collected 
by  a  different  system.  The  data  must  be  logical,  without  contradiction  or  unwarranted  variance.  This  criterion  also 
involves checking whether the supplied data fit the established reference period and other data for that Member State. 

Data 
granularity 

Relates to the level of detail at which data are collected. This is important because confusion and inaccurate decisions 
can otherwise occur. Aggregated, summarised and manipulated collections of data could offer a different meaning than 
the data implied at a lower level. An appropriate level of granularity must be defined to provide sufficient uniqueness 
and for distinctive properties to become visible. This is a requirement for the development of accurate SCOs 

Source: prepared by the study team. 

As far as the availability of the collected data are concerned, the two tables below offer a complete overview of 
the information gathered at the level of the two sub-areas analysed in relation to the area SME Growth and 
competitiveness.  

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Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

Table 66. Networking activities of SMEs (sub-area 3A): overview of data coverage 

DATA PROVIDED 

RELEVANT 
PROJECTS 

ALSO COMPLETED AND WITH 
INFO ON TOTAL COSTS 

ALSO WITH INFO ON N. 
SME AND N. EVENT 

MEMBER 
STATE 

N. 
OP 

N. 
PROJ./CALL 

N. 
OP 

N. 
PROJ./CALL 

N. OP 

N.PROJ./CALL 

N. OP 

N.PROJ./CALL 

AT 

BE 

BG 

CY 

CZ 

DE 

DK 

EE 

ES 

FI 

FR 

GR 

HR 

HU 

IE 

IT 

LT 

LU 

LV 

MT 

NL 

PL 

PT 

RO 

SE 

SI 

SK 

1 

1 

1 

- 

1 

2 

- 

- 

2 

1 

2 

1 

1 

1 

- 

2 

1 

- 

1 

1 

- 

4 

6 

- 

1 

1 

- 

10 

24 

67 

- 

3 

4616 

- 

- 

657 

202 

42 

90 

396 

2 

- 

880 

5 

- 

4 

1 

- 

37 

691 

- 

1 

7 

- 

- 

1 

- 

- 

1 

- 

- 

- 

2 

- 

2 

1 

1 

1 

- 

2 

1 

- 

- 

- 

- 

2 

6 

- 

- 

1 

- 

- 

24 

- 

- 

3 

- 

- 

- 

441 

- 

28 

90 

32 

1 

- 

880 

5 

- 

- 

- 

- 

18 

691 

- 

- 

7 

- 

- 

1 

- 

- 

1 

- 

- 

- 

2 

- 

2 

1 

1 

1 

- 

2 

1 

- 

- 

- 

- 

2 

6 

- 

- 

1 

- 

- 

24 

- 

- 

3 

- 

- 

- 

441 

- 

28 

90 

32 

1 

- 

878 

5 

- 

- 

- 

- 

14 

691 

- 

- 

6 

- 

- 

- 

- 

- 

1 

- 

- 

- 

1 

- 

1 

- 

1 

- 

- 

1 

- 

- 

- 

- 

- 

1 

6 

- 

- 

1 

- 

- 

- 

- 

- 

3 

- 

- 

- 

105 

- 

4 

- 

32 

- 

- 

6 

- 

- 

- 

- 

- 

8 

691 

- 

- 

2 

- 

TOT 

31 

7735 

21 

2220 

21 

2213 

13 

851 

Source: prepared by the study team 

Concerning sub-area 3A (networking activities of SMEs), as illustrated by the table above: 

•  The  study  teams  received  over  7 700  records  on  specific  projects  or  calls  for  proposals,  from  19 
Member States (and 31 OPs). The other 8 Member States (CY, DK, EE, IE, LU, NL, RO, SK) were 

181 

 
 
Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

contacted and declared that they do not have relevant data to provide (e.g. the monitoring system 
of some MS does not allow the extraction of sufficiently detailed data). 

•  All  in  all,  approximately  70 %  of  the  data  provided  refer  to  projects  /  calls  not  relevant  for  the 

analysis. For example: 

o  Data  provided  by  Austrian  authorities  were  not  relevant  as  they  referred  to  operations 
supporting  start-ups  or  to  operations  supporting  the  digitalisation  of  the  SMEs  or  to 
innovation networks.  

o  Data  provided  by  Bulgaria  were  considered  as  not  relevant  as  they  referred  to  projects 
supporting business growth and for the MA it was not possible to isolate the specific costs 
for supporting the internationalisation of the SMEs.  

o  Data provided by Sachsen programme (Germany) were excluded because they referred to 
projects that receive a maximum grant of EUR 5 000. Therefore, the values collected did not 
include the  total costs of the  operation but only a part of the  costs (corresponding to the 
ceiling of EUR 5 000). 

o  Data  provided  by  Mecklenburg-Vorpommern  programme  (Germany)  were  excluded 
because they referred only to projects attending fairs located in Germany (i.e. not in other 
EU MS or extra-EU countries). And therefore, the values collected did not include the costs 
for travelling in other EU Member States or extra-EU countries.  

o  Data from Czechia and Galicia (Spain) were excluded as they did not provide historical data 

but a description of SCO schemes. 

o  Data from Finland were excluded as they referred to projects identified based on the code 
of intervention 063 (i.e. Cluster support and business networks primarily benefiting SMEs) 
which may include operations such as cluster creation and support which differ from the 
networking activities as understood in the context of this study. 

o  Data  from  Latvia  were  excluded  as  they  referred  to  trainings  and  support  for  business 

incubators. 

• 

• 

In the case of 8 Member States (CZ, ES, FR, HR, IT, PL, PT, SI) we have single records providing 
detailed  information  on  (a)  the  number  of  SMEs  supported  by  each  project  /  call  and  on  (b)  the 
number of events attended by the SMEs supported. For some MS only partial data were provided 
(e.g. data provided by Finland do not include the number of SMEs supported through the single 
operation). 

In terms of granularity, 97 % of the data refers to individual projects. Only 3 % of the records refer 
to data aggregated at call level. 

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Table  67.  Consultancy  /  advisory  services  to  elaborate  an  internationalisation  strategy  (sub-area  3B): 
overview of data coverage 

DATA PROVIDED 

RELEVANT PROJECTS 

ALSO COMPLETED 
AND WITH INFO ON 
TOTAL COSTS 

ALSO WITH INFO ON N. 
SME AND N. SERVICES 

MEMBER 
STATE 

N. 
OP 

N. 
PROJ./CALL 

N. OP 

N.PROJ./CALL  N. OP  N.PROJ./CALL 

N. OP 

N.PROJ./CALL 

AT 

BE 

BG 

CY 

CZ 

DE 

DK 

EE 

ES 

FI 

FR 

GR 

HR 

HU 

IE 

IT 

LT 

LU 

LV 

MT 

NL 

PL 

PT 

RO 

SE 

SI 

SK 

- 

1 

1 

- 

1 

- 

1 

1 

2 

- 

- 

- 

- 

1 

- 

1 

- 

- 

1 

2 

- 

3 

6 

- 

1 

1 

- 

- 

10 

67 

- 

2 

- 

17 

1 

44 

- 

- 

- 

- 

5 

- 

2 

- 

- 

1 

4 

- 

8 

1 188 

- 

2 

2 

- 

- 

1 

- 

- 

1 

- 

- 

1 

1 

- 

- 

- 

- 

1 

- 

1 

- 

- 

- 

- 

- 

3 

6 

- 

1 

1 

- 

- 

10 

- 

- 

2 

- 

- 

1 

34 

- 

- 

- 

- 

5 

- 

2 

- 

- 

- 

- 

- 

8 

1 188 

- 

2 

2 

- 

- 

1 

- 

- 

1 

- 

- 

- 

1 

- 

- 

- 

- 

1 

- 

1 

- 

- 

- 

- 

- 

2 

6 

- 

1 

1 

- 

- 

9 

- 

- 

2 

- 

- 

- 

34 

- 

- 

- 

- 

5 

- 

2 

- 

- 

- 

- 

- 

7 

1 188 

- 

2 

2 

- 

- 

1 

- 

- 

1 

- 

- 

- 

1 

- 

- 

- 

- 

1 

- 

1 

- 

- 

- 

- 

- 

2 

6 

- 

1 

1 

- 

- 

9 

- 

- 

2 

- 

- 

- 

34 

- 

- 

- 

- 

5 

- 

2 

- 

- 

- 

- 

- 

7 

1 188 

- 

2 

2 

- 

TOT 

23 

1 353 

17 

1 254 

15 

1 251 

15 

1 251 

Source: prepared by the study team 

Concerning  Sub  Area  3B  (Consultancy  /  advisory  services  to  elaborate  an  internationalisation  strategy),  as 
illustrated by Table 67: 

183 

 
 
Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

•  The  study  teams  received  over  1 300  records  on  specific  projects  or  calls  for  proposals,  from  14 
Member States (and 23 OPs). The other 13 Member States (AT, CY, DE, FI, FR, GR, IE, LT, LU, NL, 
RO  and  SK)  were  contacted  and  declared  that  they  did  not  have  relevant  to  provide  (e.g.  the 
monitoring  system  of  some  Member  States  does  not  allow  the  extraction  of  sufficiently  detailed 
data). 

•  All in all, approximately 93 % of the s provided refer to projects / calls relevant to the analysis. Some 

data were considered as not relevant for various reasons. For example: 

o  Data  provided  by  Malta  were  excluded  because  they  referred  to  projects  that  receive  a 
maximum grant of EUR 5 000. Therefore, the values collected did not include the total costs 
of the operation but only a part of the costs (corresponding to the ceiling of EUR 5 000). 
o  Data provided by Denmark were excluded as it was not possible to isolate the intervention 
which contribute to the internationalisation of the SMEs from other types of consultancy / 
advisory services. 

• 

In the case of 9 Member States (BE, CZ, ES, HU, IT, PL, PT, SE, SI) we had single records providing 
detailed  information  on  (a)  the  number  of  SMEs  supported  by  each  project  /  call  and  on  (b)  the 
number of Consultancy   / advisory services per SMEs supported. 

Typologies of activities according to collected data 

Table 68 illustrates the type of eligible activities under operations supporting networking activities of SMEs. 
(sub-area 3A). The table refers only to the data by CZ, ES,  FR, HR, IT, PL, PT, SI – the 8 Member States that 
provided relevant data – on completed projects with  detailed info on the  total verified costs, the  number  of 
SMEs supported and the number of events attended by the SMEs supported.  

Table 68. Networking activities of SMEs (sub-area 3A): type of eligible activities 

MEMBER 
STATE 

CZ 

ES 

FR 

HR 

IT 

PL 

PT 

SI 

FAIR 

3 

441 

37 

878 

19 

691 

3 

INTERNATIONAL SCIENTIFIC 
CONFERENCE 

PARTNERSHIP EXCHANGE 

1 

1 

28 

1 

3 

Source: prepared by the study team 

As illustrated by Table 68 above, 7 out of 8 MS that provided relevant data, did support (exclusively or mainly) 
the participation to trade fairs. The only exception is FR which supported partnership exchanges. 

Additional  information  on  the  adopted  modality  of  attendance  to  the  above-mentioned  activities  were  also 
requested  to  the  data  owners.  In  particular,  respondents  should  specify  if  the  relevant  projects  /  calls  for 

184 

 
 
 
 
 
 
 
 
 
 
 
 
 
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proposals reported in the forms funded the participation to these activities in presence, remotely or in hybrid 
form. All records provided by the 8 MS referred to networking activities held in presence.  

Table 69 illustrates the type of eligible activities under operations supporting SMEs recurring to consultancy / 
advisory  services  to  elaborate  an  internationalisation  strategy  (sub-area  3B).  The  table  refers  only  to  the   
provided by BE, CZ, ES, HU, IT, PL, PT, SE, SI –the 9 Member States that provided relevant data – on completed 
projects with detailed information on the total verified costs, the number of SMEs supported and the number 
of consultancy / advisory services supported. 

Table 69. Consultancy / advisory services to elaborate an internationalisation strategy (sub-area 3B): type of 
eligible activities 

MEMBER 
STATE 

EXPORT STRATEGY 

MARKET RESEARCH 

EXPORT STRATEGY + MARKET 
RESEARCH 

7 

2 

BE 

CZ 

ES 

HU 

IT 

PL 

PT 

SE 

SI 

3 

34 

5 

1188 

2 

2 

2 

8 

Source: prepared by the study team 

As illustrated by Table 69, 8 out of 9 MS that provided relevant data, did support operations for the definition 
of a market research. In the case of CZ and PL these operations cover both the elaboration of a market research 
and  of  an  export  strategy.  Relevant  data  provided  by  IT  only  refer  to  project  supporting  the  elaboration  of 
export strategies. 

Costs incurred according to collected data 

This section illustrates the  type of eligible costs under operations supporting networking activities of SMEs. 
(sub-area 3A). The analysis refers only to the data provided by CZ, ES, FR, HR, IT, PL, PT, SI – the 8 Member 
States that provided relevant data on completed projects – with detailed information on the total verified costs, 
the  number  of  SMEs  supported  and  the  number  of  events  attended  by  the  SMEs  supported.  The  following 
categories of costs are eligible: 

costs related to renting stands; 

• 
•  promotion costs (e.g. production of booklets for the exhibition);  
• 
• 
• 
• 
• 

costs related to registering for the event; 
travel and accommodation costs; 
costs of the transportation of the materials; 
staff costs; 
costs of specialised external services (e.g. for translation services). 

185 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

As regards the eligible costs under operations supporting SMEs recurring to consultancy / advisory services to 
elaborate an internationalisation strategy (sub-area 3B), the analysis refers only to the data provided by BE, CZ, 
ES, HU, IT, PL, PT,  SE, SI  –  the  9 Member  States  that  provided relevant data, on completed projects  –  with 
detailed  info  on  the  total  verified  costs,  the  number  of  SMEs  supported  and  the  number  of  consultancy  / 
advisory services supported. The following categories of costs are eligible: 

staff costs; 

• 
•  direct costs other than staff (e.g. travel, trademarks, acquisition of information, equipment, etc.); 
• 

indirect costs. 

Outputs and results produced according to collected data 

Table  70  illustrates  the  indicators  used  to  monitor  the  operations  supporting  networking  activities  of  SMEs 
(sub-area 3A). The table refers only to the data provided by BE, CZ, ES, HU, IT, PL, PT, SE, SI – the 9 Member 
States that provided relevant data, on completed projects  – with detailed info on the total verified costs, the 
number of SMEs supported and the number of consultancy / advisory services supported.  

Table 70. Networking activities of SMEs (sub-area 3A): type of indicators 

D
E
T
R
O
P
P
U
S
S
E
M
S
E
H
T
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B
D
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105 

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880 

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7 

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691 

3 

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MEMBER 
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Source: prepared by the study team 

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U
S
S
E
S
I
R
P
R
E
T
N
E

N
O
I
T
A
S
I
L
A
N
O
I
T
A
N
R
E
T
N

I

0 

0 

0 

0 

0 

8 

0 

0 

N
O
I
T
A
S
I
L
A
N
O
I
T
A
N
R
E
T
N

I

F
O
E
P
O
C
S
E
H
T
N

I

D
E
T
R
O
P
P
U
S
S
M
R
I
F

0 

0 

0 

0 

0 

9 

0 

0 

L
E
V
E
L
L
A
N
O
I
T
A
N
A
N
O
S
T
C
E
J
O
R
P

N
O
I
T
O
M
O
R
P
D
N
A
N
O
I
T
A
M
R
O
F
N

I

D
E
T
R
O
P
P
U
S
F
O
R
E
B
M
U
N

0 

0 

0 

0 

0 

6 

0 

0 

N
O
I
T
O
M
O
R
P
D
N
A
N
O
I
T
A
M
R
O
F
N

I

D
E
T
R
O
P
P
U
S
F
O
R
E
B
M
U
N

L
E
V
E
L
L
A
N
O
I
T
A
N
R
E
T
N

I

N
A
F
O
S
T
C
E
J
O
R
P

0 

0 

0 

0 

0 

10 

0 

0 

S
R
I
A
F
D
N
A
S
N
O
I
T
I
B
I
H
X
E
N

I
S
N
O
I
T
A
P
I
C
I
T
R
A
P
F
O
R
E
B
M
U
N

3 

0 

0 

0 

0 

0 

0 

0 

D
A
O
R
B
A

186 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

As illustrated by Table 70 the 8 Member States that provided relevant data, use different types of indicators to 
monitor operations supporting networking activities of SMEs.  

The two indicators most used were ‘number of SMEs supported’, and ‘number of events attended by the SMEs 
supported’, which were used by all 8 Member States. 

The use of common ERDF/CF output indicators is limited to: 

3 Member States in the case of CO01 (FR, IT, SI); 
2 Member States in the case of CO02 (IT, SI); 

• 
• 
•  only HR in the case of CO06; 
•  only FR in the case of CO26; 
• 

several Member States use programme-specific indicators. 

Table 71  illustrates the indicators used to monitor the operations supporting SMEs recurring to consultancy / 
advisory services to elaborate an internationalisation strategy (sub-area 3B). The table refers only to the data 
provided  by BE, CZ, ES, HU, IT, PL, PT, SE, SI – the 9 Member States that provided relevant data, on completed 
projects  –  with  detailed  info  on  the  total  verified  costs,  the  number  of  SMEs  supported  and  the  number  of 
consultancy / advisory services supported. 

Table 71. Consultancy / advisory services to elaborate an internationalisation strategy (sub-area 3A): type of 
indicators 

S
E
M
S
R
E
P
D
E
T
R
O
P
P
U
S
S
E
C
I
V
R
E
S

/

Y
C
N
A
T
L
U
S
N
O
C
Y
R
O
S
I
V
D
A
F
O
R
E
B
M
U
N

10 

2 

34 

5 

2 

8 

D
E
T
R
O
P
P
U
S
S
E
M
S
F
O
R
E
B
M
U
N

10 

2 

34 

5 

2 

8 

1188 

1188 

2 

2 

2 

2 

)
1
0
O
C

(

T
R
O
P
P
U
S
G
N
I
V
I
E
C
E
R
S
M
R
I
F

0 

0 

0 

0 

2 

0 

0 

0 

2 

N
O
I
T
A
S
I
L
A
N
O
I
T
A
N
R
E
T
N

I
F
O
E
P
O
C
S
E
H
T

N

I

D
E
T
R
O
P
P
U
S
S
E
S
I
R
P
R
E
T
N
E
F
O
R
E
B
M
U
N

0 

0 

0 

0 

2 

6 

0 

0 

0 

S
T
C
A
R
T
N
O
C
E
D
A
R
T
N
G
I
E
R
O
F
F
O
R
E
B
M
U
N

0 

0 

0 

0 

0 

7 

0 

2 

0 

N

I

D
E
T
R
O
P
P
U
S
S
E
S
I
R
P
R
E
T
N
E
Y
B
D
E
N
G
I
S

N
O
I
T
A
S
I
L
A
N
O
I
T
A
N
R
E
T
N

I
F
O
D
L
E
I
F
E
H
T

S
T
C
A
T
N
O
C
S
S
E
N
I
S
U
B
F
O
R
E
B
M
U
N

0 

0 

0 

0 

0 

4 

0 

0 

0 

)
4
0
O
C

(

T
R
O
P
P
U
S

L
A
I
C
N
A
N
I
F
-
N
O
N
G
N
I
V
I
E
C
E
R
S
M
R
I
F

0 

0 

0 

2 

0 

1 

0 

0 

0 

G
N
I
T
A
P
I
C
I
T
R
A
P
S
E
S
I
R
P
R
E
T
N
E
F
O
R
E
B
M
U
N

S
E
G
N
A
H
C
S
S
E
C
O
R
P

D
N
A
L
A
N
O
I
T
A
Z
I
N
A
G
R
O
D
E
C
U
D
O
R
T
N

I

Y
E
V
R
U
S
E
H
T
N

I

0 

0 

0 

0 

0 

1 

0 

0 

0 

T
A
H
T
S
E
S
I
R
P
R
E
T
N
E
F
O
R
E
B
M
U
N

0 

0 

0 

0 

0 

4 

0 

0 

0 

N
O
I
T
A
S
I
L
A
N
O
I
T
A
N
R
E
T
N

I

G
N
I
T
R
O
P
P
U
S

S
N
O
I
T
A
C
I
L
P
P
A

/

S
M
R
O
F
T
A
L
P
F
O
R
E
B
M
U
N

0 

0 

0 

0 

0 

1 

0 

0 

0 

T
C
E
J
O
R
P
E
H
T
R
E
D
N
U
D
E
N
E
P
O
S
E
R
T
N
E
C

N
O
I
T
A
S
I
L
A
N
O
I
T
A
N
R
E
T
N

I
F
O
R
E
B
M
U
N

0 

0 

0 

0 

0 

1 

0 

0 

0 

T
R
O
P
P
U
S
D
E
V
I
E
C
E
R
H
C
I
H
W
S
E
I
N
A
P
M
O
C

N

I
S
E
E
Y
O
L
P
M
E
W
E
N
F
O
R
E
B
M
U
N

0 

0 

0 

0 

0 

0 

0 

1 

0 

R
I
E
H
T
N
E
H
T
G
N
E
R
T
S
O
T
D
E
T
A
R
E
P
O
O
C

E
V
A
H
H
C
I
H
W
S
E
I
N
A
P
M
O
C
F
O
R
E
B
M
U
N

0 

0 

0 

0 

0 

0 

0 

1 

0 

)
2
0
O
C

(
S
T
N
A
R
G
G
N
I
V
I
E
C
E
R
S
M
R
I
F

0 

0 

0 

0 

2 

0 

0 

0 

2 

MEMBER 
STATE 

BE 

CZ 

ES 

HU 

IT 

PL 

PT 

SE 

SI 

Source: prepared by the study team 

D
E
R
E
F
F
O
S
A
E
R
A
E
C
I
V
R
E
S
F
O
R
E
B
M
U
N

Y
T
I
L
I
B
A
E
V
I
T
A
V
O
N
N

I

N
A
L
P
S
S
E
N
I
S
U
B
E
H
T
O
T
G
N
D
R
O
C
C
A

I

0 

2 

0 

0 

0 

0 

0 

0 

0 

187 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Study to develop EU level SCOs and other EU level results-based tools. Final Study Report 

As illustrated by  Table 71, the 9 MS that provided relevant data, use different types of indicators to monitor 
operations supporting consultancy / advisory services to elaborate an internationalisation strategy.  

The indicators most used were ‘number of SMEs supported’, and ‘number of advisory / consultancy services 
supported per SMEs’, which were used by all 9 Member States that provided relevant data. 

The use of common ERDF/CF output indicators is limited to: 

• 
• 
• 
• 

2 Member States in case of CO01 (IT, SI); 
2 Member States in case of CO02 (IT, SI); 
2 Member States in case of CO04 (HU, PL); 
several Member States use programme-specific indicators. 

188 

 
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K
N
-
0
9
-
2
3
-
4
0
8
-
E
N
N

-