Etude Cushman et Wakefield

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COMPARISON OF PRIME LOCATIONS

FOR

EUROPEAN

DISTRIBUTION AND LOGISTICS

2009

Abridged edition

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Comparison of prime locations for European logistics and Distribution 2009

This report has been produced by Cushman & Wakefield LLP, in collaboration with Logistics in Wallonia and AWEX, for information purposes. It is not intended to be a complete description of the

markets or developments to which it refers. The report uses information obtained from public sources which Cushman & Wakefield LLP believe to be reliable, but we have not verified such

information and cannot guarantee that it is accurate and complete. The report also refers to these economic sources: Eurostat, Consensus Economics Inc.; The Economist; Reuters; Experian Business

Strategies; Centre for Business & Economic Research. No warranty or representation, express or implied, is made as to the accuracy or completeness of any of the information contained herein and

Cushman & Wakefield LLP shall not be liable to any reader of this report or any third party in any way whatsoever. All expressions of opinion are subject to change. The prior written consent of

Cushman & Wakefield LLP, Logistics in Wallonia or AWEX is required before this report can be reproduced in whole or in part.

Comparison of prime locations for European logistics and Distribution 2009

EXECUTIVE SUMMARY ........................................................................... 3

INTRODUCTION........................................................................................ 5

METHODOLOGY....................................................................................... 6

ELEMENTS OF THE ‘RANKED-MATRIX’. ............................................... 6

NUTS-1 IS BUILT UP FROM NUTS-2 DATA............................................ 6

WEIGHTS, SOURCE MATERIAL AND SENSITIVITY.............................. 7

EXAMPLE OF A MATRIX-ELEMENT ....................................................... 8

DOMAIN: COSTS ...................................................................................... 8

OVERALL RESULTS .............................................................................. 12 The choice of the regions. ................................................................................................................................................. 12 Ranking byNUTS-2 region.................................................................................................................................................. 13 Ranking by NUTS-1 region................................................................................................................................................. 15

A LOOK AT THE FUTURE...................................................................... 16 Forecasted Matrix 2020 by NUTS-2 region. ...................................................................................................................... 21 Forecasted Ranking by NUTS-1 region. ........................................................................................................................... 24

ATTACHMENTS...................................................................................... 25

ATTACHMENT A: GLOSSARY .............................................................. 25

ATTACHMENT B: MATRIX RANKING BY NUTS-2 REGION. ............... 27

ATTACHMENT C: MATRIX RANKING BY NUTS-1 REGION. ............... 29

ATTACHMENT D: FORECASTED MATRIX NUTS-2 REGIONS 2020... 30

ATTACHMENT E: FORECASTED MATRIX NUTS-1 REGIO’ S 2020. .. 32

ATTACHMENT G: CALCULATION TABLE BUYING POWER IN THE 3-HOUR DRIVETIME PERIMETER.............................................................. 37

ATTACHMENT H: THEMATIC MAPS OF THE DOMAINS AND THE TOTAL SCORES, BY NUTS-2 REGION .................................................. 40

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Executive Summary This report makes a ranking of top locations for distribution and logistics in Europe, based upon macro-economic parameters. This is done by using a ranked-matrix method for 61 European regions and for the optimal location of a European Distribution Centre ( EDC, not a regional logistics centre). As in previous reports, published in 2004 and 2006 by the Flanders Institute for Logistics, several Belgian provinces come in the top of this ranking. Liège comes out as the number 1 location, closely followed by Limburg (B), Hainaut, and Nord-Pas-de-Calais. The main reasons for this top-ranking are : - excellent access to the main European markets ( the core West-German markets like the Rurhgebiet, the in the Netherlands, the core Benelux markets like Randstad/Antwerp/Brussels , Paris/Ile-de-France and the greater London area) - a central geographic location that is optimal to cover a wide range of European markets - top transport infrastructure and volume, close to main ports or with good multimodal links to these ports - low costs for land, warehouses and labour - labour force that is available, highly productive, skilled for supply chain jobs and with a good language knowledge The top-15 consists of regions from Belgium, Northern France (Nord-Pas-de-Calais and Alsace) and Western Germany ( Düsseldorf, Koblenz, Köln and Arnsberg). Regions from the Netherlands are, given their history as important logistics locations, often perceived as top regions for distribution; in this macro-economic ranking they score relatively average ( Limburg (NL)/ Venlo comes on 23rd place, Noord-Brabant/Eindhoven 31st place, Zuid-Holland/ Rotterdam 37th place ). The main reasons for this relatively low score of the Dutch regions are : - relatively high costs for land and warehouses, combined with a severe urban planning system that makes it difficult to guarantee the required space for future logistics property development - road congestion problems, especially in the Randstad areas - labourforce availability has proven to be relatively limited in periods of strong economic activity The matrix used in this study is based upon the quantifiable variables that play a role in the decision to locate an EDC; the relative weight of the variables in the matrix is based upon surveys amongst decision makers, like the European Cities Monitor survey that Cushman & Wakefield publishes on a yearly basis. Over the years these weights have shifted a little: the relative importance of Labour ( Available Labourforce / Labour productivity ) was given more weight ( 9%) than in the 2004 and 2006 studies, especially because available labourforce was a growing problem over 2007 and 2008.

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Cushman & Wakefield 2009

Comparison of prime locations for European logistics and distribution 2009

Ranking Nuts-2 regions 2009 CostsTransport

system Accessibility Supply Labour Know-how SCORERanking

2009Weight % 21% 29% 29% 9% 9% 3% Total

LIEGE 4.1 1.5 1.0 2.4 2.6 2.5 2.1 1LIMBURG -B (Genk-Hasselt) 3.6 2.0 1.2 1.2 2.9 1.7 2.1 2

HAINAUT (Charleroi) 3.2 2.2 1.7 1.0 2.1 3.3 2.2 3NORD - PAS-DE-CALAIS (Lille) 2.8 2.5 3.0 2.1 2.5 3.8 2.7 4

NAMUR 3.7 2.4 2.0 4.3 2.1 3.5 2.7 5Luxembourg - B( Arlon) 3.3 3.4 1.5 2.3 3.6 4.0 2.7 6ALSACE (Strasbourg) 3.7 2.8 2.1 2.8 3.8 3.8 2.9 7

OOST-VLAANDEREN (Gent) 5.8 2.0 2.0 2.5 3.2 2.0 2.9 8ANTWERPEN 7.4 1.5 2.0 2.4 2.0 1.0 3.0 9ARNSBERG 4.7 3.6 1.5 2.0 4.0 3.3 3.1 10

KÖLN 8.6 1.8 0.7 3.0 3.4 2.5 3.1 11KOBLENZ 6.0 3.1 1.0 2.8 4.5 3.0 3.2 12

….MAZOWIECKIE (Warszawa) 4.7 5.2 8.2 1.8 4.5 6.0 5.6 53

TIROL (Innsbruck) 9.0 4.6 5.1 5.0 4.4 4.3 5.7 54GREATER LONDON 12.1 2.6 3.9 6.0 7.6 2.0 5.7 55

SW SCOTLAND (Glasgow) 8.8 4.1 7.1 3.5 4.5 3.0 5.9 56SYDSVERIGE (Malmö)/Öresund 9.0 4.8 6.8 4.0 4.3 3.0 6.1 57

VASTSVERIGE (Göteborg) 8.4 5.5 7.3 4.3 4.1 3.0 6.3 58CATALUNA (Barcelona) 10.4 3.9 7.8 4.6 3.2 4.0 6.4 59

COM. DE MADRID 10.7 4.8 10.0 3.8 2.6 5.0 7.3 60LISBOA VALE DO TEJO 6.8 4.9 12.0 3.5 5.0 5.5 7.3 61

median score 6.7 3.3 3.0 2.8 3.9 3.0 4.1 For a location decision, the actual situation is important, but it is even more important to know what the future will bring in terms of infrastructure development, land supply, expected warehouse rent evolution etc. That is why a forecast of the matrix data was developed for the time horizon of 2020. According to this forecast Liège will not be able to hold its nr 1 position : it is extremely well located, but the limited availability of land give this region a slight disadvantage versus Hainaut who will be nr1 in our view. This reflects the growing importance of good transport infrastructure towards markets south of the actual core European logistics regions; the Seine-Nord Europe canal junction that will upgrade the inland waterway between the Paris region and the North of France and Belgium also increases the score of Nord-Pas-de-Calais and Hainaut. The gradual shift of the centre of gravity of the European markets towards central Europe will result in a rise of German regions like Köln and Düsseldorf; the good geographical position towards main German and central-European markets will keep Limburg (B) and Liège in a strong 2 and 3 position:

Forecast 2020 NUTS-2 regions CostsTransport

system Accessibility Supply LabourKnow-how SCORE

Forecasted Ranking 2020

Weight % 19% 27% 27% 8% 15% 3% TotaalHAINAUT (Charleroi) 5.9 2.4 1.8 1.0 2.3 3.0 2.8 1

LIMBURG -B (Genk-Hasselt) 5.9 2.2 1.7 1.3 3.3 1.7 2.9 2LIEGE 6.7 1.9 1.2 2.3 3.3 2.5 2.9 3

NORD - PAS-DE-CALAIS (Lille) 6.6 2.3 2.5 1.3 2.2 3.5 3.1 4DÜSSELDORF 9.3 1.8 0.9 3.3 2.3 2.5 3.2 5

KÖLN 10.3 1.7 0.8 3.3 2.3 2.5 3.3 6ALSACE (Strasbourg) 6.1 2.8 2.1 2.0 4.3 3.5 3.4 7

ARNSBERG 7.3 3.5 1.4 2.0 3.0 3.3 3.5 8VLAAMS BRABANT (Vilvoorde) 10.1 2.1 1.5 2.8 1.7 1.8 3.5 9

SAARLAND 6.7 3.6 2.1 1.7 2.8 3.3 3.5 10

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Introduction This report gives a comparison of European top-regions for logistics, based on macro-economic factors with an impact on distribution and logistics. The Ranked Matrix methodology ( see further ‘Methodology’) enables a quantitative comparison of strengths and weaknesses of each region. Every two years, Cushman & Wakefield (C&W) publish the European Distribution Report, which maps the different European countries in terms of logistics and distribution. In the perspective of advising players of the logistics sector in their localisation strategy, the data matrix used in the present study is based upon regional data (Eurostat NUTS-1 and NUTS-2). In Belgium, regions in NUTS-1 are concordant with the actual Belgian regions, and NUTS-2 regions are concordant with the province level. NUTS-3 regions, which in Belgium are concordant with “arrondissements”, are not used in this study. This study uses matrix data from both NUTS-1 and NUTS-2 regional levels in order to produce an analysis of Wallonia’s strengths and weaknesses towards competing regions such as the Netherlands, Brussels and Flanders, Northern France, Germany’s Länder, and other additional European top-regions for logistics. The present study is an actualisation of previous studies delivered in the frame of missions for the Flanders Institute for Logistics in 2004 and 2006.

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Methodology

Elements of the ‘ranked-matrix’.

The following domains are being accounted through the use of a Ranked Matrix (see also Attachment A, Glossary): • Costs of warehouse spaces, land for professional use and labour (weights 21% in the overall matrix) • Transportation system: characteristics of the different transportation modes (weights 29% in the overall matrix) • Accessibility to markets (weights 29% in the overall matrix) • Offer in terms of logistics property and land provision (weights 8% in the overall matrix) • Labour: offer in terms of workforce and productivity (weights 9% in the overall matrix). Note: oppositely to previous similar studies (for the Flanders Institute for Logistics in 2004 and 2006), the weight of this domain has been increased, considering the lack of workforce which under the current critical economic conditions has become an issue in several regions. • Know-How: education and trainings in logistics, and language knowledge (weights 3% in the overall matrix. The matrix uses weights concordant with the ones used by C&W in any average EDC demand. The weights reflect the importance given by decision makers to the various location factors. These are followed and updated according to yearly surveys led by C&W, such as the European Cities Monitor. Like in 2006, the matrix-element “Population proximity” was replaced with “Spending power within a 180 min. drive-time”. Not only does this element give a better measure of the local markets, but it also makes the matrix a better index for more regional-driven distribution centres. In Central Europe, this factor shows a double leverage-effect, as the spending power increases more proportionally then in Western Europe, and as the road-network develops so fast that more people are being covered by the considered 3 hour perimeter.

NUTS-1 is built up from NUTS-2 data The NUTS-1 matrix is calculated according to a bottom-up method: scores of lower level (NUTS-2) are calculated first. For the constitution of the NUTS-1 matrix, the arithmetical average of NUTS-2 regions in the NUTS-1 territory gets calculated. Like in the 2006 study, this study was broadened to the NUTS-2 level (oppositely to the 2004 study), adding 23 extra regions from the EU-27 to the corpus. These are isolated NUTS-2 regions, in the sense that surrounding regions have not been calculated into the matrix, with the consequence that we were not able to calculate an overall average of the concordant NUTS-1 regions for

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these regions. The 23 extra regions are located lower in the NUTS-2 ranking, Prague having the best score among them, yet reaching the 29th place only (see below). Hence, this is not an obstacle to the constitution of a ranking of NUTS-1 regions.

Weights, Source material and Sensitivity Attachment F describes the domains, and matrix-elements of these domains with their respective weights. It also gives a view on the source material used in the study, as well as an indication of their sensitivity. As far as sensitivity of an element is concerned, one can only give an indication of it, since this is about a ranking of data which vary from region to region in a non linear way. Wherever a satisfying level of linearity was found among data, these were translated into points and added to the score. For some of the matrix elements, different data were put into balance. This is the case of “Available workforce”, where both unemployment figures (as an indication of immediate availability) and the percentage of younger people were taken into account in order to calculate the future availability of workforce. Yet such elements cannot be assigned with univocal indicators of sensitivity. As this analysis of strengths and weaknesses uses a Ranked Matrix, whenever possible, privilege was given to: • quantifiable elements • elements which allow cross-region and cross-border comparison. Domains which, although important in logistics and distribution, could not be taken into account for the reasons explained here above, were: • quantity and fluidity in the deliverance of permits and administrative procedures (such as toll procedures) • taxation, rulings, etc. Since figures on permits and administrative procedure are hardly available, not only is their actual situation here at stake, but also the perception players and potential players may have of them.

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Example of a matrix-element

In this chapter we show an example of how the matrix elements are being calculated. In this abridged edition of the study we only explain the details of this matrix-element. The detailed scores for each region can be found in Attachments B to E for all matrix elements. The detailed explanation on the matrix-elements can be found in the full version of this study.

Domain: Costs

Matrix-element: Rents

The matrix-element “Rents” gives a ranking of the rental values for warehouse spaces in the considered regions. Theses values were calculated on the basis of a standard warehouse space of approximately 10,000 sq.m., and according to the current norms in logistics property (minimum 10.5 meter free height, 6-ton tolerance floor, minimum one loading dock per 1,000 sq.m., sprinklers, partition, etc.). Values used in the matrix were taken from the C&W Industrial Space Across the World 2009 study. One notes that Ile-de-France barely reaches a rental value of 51€/sq.m./year, which translates in a score of 7.4 for his matrix-element. These rents for semi-industrial property in the broader region of Paris remain exceptionally low, especially when compared with prices in the residential or office property in that region. This trend was already showing in the 2004 and 2006 editions of our study. This can only be explained by the active policies led by the French government regarding the creation of a sufficient offer (sufficient building land, commercialisation through governmental institutions, which can be compared with “intercommunales” in Wallonia). Yet, such policies are not tenable on the longer term in the immediate surroundings of a major city like Paris. This region will inevitably achieve a higher score in the years to come (see below). In the matrix, scores gain one point for every average increase 5€/sq.m./year in the rental value. This indicator is called “Sensitivity indicator” of the matrix-element (see also Attachment F for an overview). The sensitivity is provided whenever there is sufficient linearity in the data, and whenever one indicator is taken into account. This sensitivity is a useful tool, which in the future will allow a monitoring of what will or could be the effects of an increase of rental values in Wallonia property market on its current position in the competition (see also chapter “A Look at the Future”, which provides a prognosis of each matrix-element for 2020). Weight: within the “Costs” domain, this element was given a 38% weight. Pondered with the 21% of the overall domain, this means the matrix-element “Rents” has a 7.7% weight in the overall matrix. Regions achieving the best scores for this element are: • Several Belgian regions such as Hainaut, Liège, Luxembourg and Limburg • Zeeland and other provinces of Northern Netherlands • Regions in Northern France achieve scores going from average to good (Champagne-Ardenne and Picardie have a better score than Nord-Pas-de-Calais, Provence and Alsace).

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Regions presenting worse scores for this element include: • Several English regions (especially the Greater London area) • Top-regions in Spain and Italy. The overall relatively high rental values in these regions, combined with a strong demand for warehouse and local distribution solutions explains this phenomenon. • Many Dutch regions find themselves in the lower section of the ranking regarding this element. Some exceptional values (outliers) like Greater London were assigned with a weakened score in order to maintain them below the 12.5 limit. The following calculation table gives an example of a full translation of rents into matrix scores:

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NUTS code NUTS2 REGION Rent 2009 Score RentAT13 WIEN 54 7.8AT32 SALZBURG 46 4.6AT33 TIROL (Innsbruck) 48 5.4BE10 BRUSSELS CAP.REGION 47 4.8BE21 ANTWERPEN 43 3.4BE22 LIMBURG (B) 36 0.6BE23 OOST-VLAANDEREN 38 1.0BE24 VLAAMS BRABANT 55 8.0BE25 WEST-VLAANDEREN 42 3.0BE31 BRABANT WALLON 48 5.4BE32 HAINAUT 35 0.2BE33 LIEGE 36 0.6BE34 LUXEMBOURG (B) 35 0.2BE35 NAMUR 38 1.0CZ01 PRAHA 45 4.0DE21 OBERBAYERN (München) 78 11.6DE3 BERLIN 60 8.2DE6 HAMBURG 72 11.0DE71 DARMSTADT (Frankfurt) 72 11.0DEA1 DÜSSELDORF 66 10.4DEA2 KÖLN 60 8.2DEA3 MUNSTER 48 5.4DEA5 ARNSBERG 47 4.8DEB1 KOBLENZ 49 6.6DEB2 TRIER 52 7.6DEB3 RHEINHESSEN-PFALZ (Kaiserslautern) 50 6.8DEC0 SAARLAND 48 5.4ES3 COM. DE MADRID 87 12.0ES51 CATALUNA (Barcelona) 81 11.8FR10 ILE DE France ( Paris) 51 7.4FR21 CHAMP.-ARDENNE (Reims) 38 1.0FR22 PICARDIE (Amiens) 40 1.6FR30 NORD - PAS-DE-CALAIS (Lille) 41 2.8FR41 LORRAINE ( Nancy ) 48 5.4FR42 ALSACE (Strasbourg) 45 4.0FR71 RHONE-ALPES (Lyon) 47 4.8FR82 PROVENCE-ALPES COTE D'AZUR ( Marseille) 44 3.8HU01 KOZEP-MAGYAR.(Budapest) 45 4.0IT2 LOMBARDIA (Milano) 64 9.8IT6 LAZIO (Roma) 62 9.6LU00 LUXEMBOURG (GRAND DUCHE) 60 8.2NL11 GRONINGEN REGION 40 1.6NL12 FRIESLAND (Leeuwarden) 40 1.6NL13 DRENTHE (Emmen) 40 1.6NL21 OVERIJSSEL ( Enschede) 40 1.6NL22 GELDERLAND (Nijmegen) 50 6.8NL23 FLEVOLAND (Lelystad) 40 1.6NL31 UTRECHT REGION 65 10.2NL32 NOORD-HOLLAND (Amsterdam) 60 8.2NL33 ZUID-HOLLAND (Rotterdam) 60 8.2NL34 ZEELAND (Terneuzen) 43 3.4NL41 NOORD-BRABANT (Eindhoven) 60 8.2NL42 LIMBURG -NL ( Venlo) 50 6.8PL07 MAZOWIECKIE (Warszawa) 60 8.2PT13 LISBOA VALE DO TEJO 48 5.4SE04+Cop.DK SYDSVERIGE (Malmö)/Öresund 73 11.4SE05 VASTSVERIGE (Göteborg) 64 9.8SK01 BRATISLAVSKY KRAJ 42 3.0UK73 WEST MIDLANDS (Birmingham) 70 10.6UKI1&2 GREATER LONDON 99 12.2UKM3 SW SCOTLAND (Glasgow) 70 10.6

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Overall Results In the total score of the matrix, all elements with teir respective score are brought together in one figure. It is not a measure for the direct local logistics activity but a measurement of the potential attractiveness of a region for the location ofan EDC, according to measurable macro-economic criteria.

The choice of the regions. Cushman & Wakefield have published the European Disitribution Report on a regular basis; this reports traditionally gave a ranking of countries in and around the so called “Blue Banana” area: the spine of European economic activity and logistics. Over the years a number of ‘Key European Hubs’ have been added to this report and into the regional data matrix of Cushman & Wakefield, more specifically with the development of Central Europe. Since 2006 Cushman & Wakefield has expanded its regional reports to 61 NUTS-2 regions, covering most of the ‘Key European Hubs’ of following map:

Source: Cushman & Wakefield, European Distribution Report 2006 & 2008

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Ranking byNUTS-2 region. The full matrix by NUTS-2 region can be found in Attachment B. The summary of gives following table:

Ranking Nuts-2 regions 2009 CostsTransport

system Accessibility Supply Labour Know-how SCORERanking

2009Weight % 21% 29% 29% 9% 9% 3% Total

LIEGE 4.1 1.5 1.0 2.4 2.6 2.5 2.1 1LIMBURG -B (Genk-Hasselt) 3.6 2.0 1.2 1.2 2.9 1.7 2.1 2

HAINAUT (Charleroi) 3.2 2.2 1.7 1.0 2.1 3.3 2.2 3NORD - PAS-DE-CALAIS (Lille) 2.8 2.5 3.0 2.1 2.5 3.8 2.7 4

NAMUR 3.7 2.4 2.0 4.3 2.1 3.5 2.7 5Luxembourg - B( Arlon) 3.3 3.4 1.5 2.3 3.6 4.0 2.7 6ALSACE (Strasbourg) 3.7 2.8 2.1 2.8 3.8 3.8 2.9 7

OOST-VLAANDEREN (Gent) 5.8 2.0 2.0 2.5 3.2 2.0 2.9 8ANTWERPEN 7.4 1.5 2.0 2.4 2.0 1.0 3.0 9ARNSBERG 4.7 3.6 1.5 2.0 4.0 3.3 3.1 10

KÖLN 8.6 1.8 0.7 3.0 3.4 2.5 3.1 11KOBLENZ 6.0 3.1 1.0 2.8 4.5 3.0 3.2 12

….MAZOWIECKIE (Warszawa) 4.7 5.2 8.2 1.8 4.5 6.0 5.6 53

TIROL (Innsbruck) 9.0 4.6 5.1 5.0 4.4 4.3 5.7 54GREATER LONDON 12.1 2.6 3.9 6.0 7.6 2.0 5.7 55

SW SCOTLAND (Glasgow) 8.8 4.1 7.1 3.5 4.5 3.0 5.9 56SYDSVERIGE (Malmö)/Öresund 9.0 4.8 6.8 4.0 4.3 3.0 6.1 57

VASTSVERIGE (Göteborg) 8.4 5.5 7.3 4.3 4.1 3.0 6.3 58CATALUNA (Barcelona) 10.4 3.9 7.8 4.6 3.2 4.0 6.4 59

COM. DE MADRID 10.7 4.8 10.0 3.8 2.6 5.0 7.3 60LISBOA VALE DO TEJO 6.8 4.9 12.0 3.5 5.0 5.5 7.3 61

median score 6.7 3.3 3.0 2.8 3.9 3.0 4.1 As in previous reports, published in 2004 and 2006 by the Flanders Institute for Logistics, several Belgian provinces come in the top of this ranking. Liège comes out as the number 1 location, closely followed by Limburg (B), Hainaut, and Nord-Pas-de-Calais. The main reasons for this top-ranking are : - excellent access to the main European markets ( the core West-German markets like the Rurhgebiet, the in the Netherlands, the core Benelux markets like Randstad/Antwerp/Brussels , Paris/Ile-de-France and the greater London area) - a central geographic location that is optimal to cover a wide range of European markets - top transport infrastructure and volume, close to main ports or with good multimodal links to these ports - low costs for land, warehouses and labour - labour force that is available, highly productive, skilled for supply chain jobs and with a good language knowledge The top-15 consists of regions from Belgium, Northern France (Nord-Pas-de-Calais and Alsace) and Western Germany ( Düsseldorf, Koblenz, Köln and Arnsberg). Regions from the Netherlands are, given their history as important logistics locations, often perceived as top regions for distribution; in this macro-economic ranking they score relatively average ( Limburg (NL)/ Venlo comes on 23rd place, Noord-Brabant/Eindhoven 31st place, Zuid-Holland/ Rotterdam 37th place ). The main reasons for this relatively low score of the Dutch regions are : - relatively high costs for land and warehouses, combined with a severe urban planning system that makes it difficult to guarantee the required space for future logistics property development - road congestion problems, especially in the Randstad areas - labourforce availability has proven to be relatively limited in periods of strong economic activity The matrix used in this study is based upon the quantifiable variables that play a role in the decision to locate an EDC; the relative weight of the variables in the matrix is based upon surveys amongst decision makers, like the European Cities Monitor survey that Cushman & Wakefield publishes on a yearly basis.

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Over the years these weights have shifted a little: the relative importance of Labour ( Available Labourforce / Labour productivity ) was given more weight ( 9%) than in the 2004 and 2006 studies, especially because available labourforce was a growing problem over 2007 and 2008. For the analysis of strengths and weaknesses of each region one can consult the matrix in Attachment B with all the detail of the each score of the domain sub-elements. In this matrix relative strengths and weaknesses are visually represented in green and red: - good scores have a green background ( if < than 50% of the median score ); - bad scores have a red background ( if > than 150% of the median score ); The total score and the 6 big “Domains” of this matrix , are also translated into thematical maps in Attachment H.

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Ranking by NUTS-1 region. A NUTS-1 region is an aggregation of one or more NUTS-2 region into a higher geographical territory. The ranking by NUTS-1 region is calculated by means of the average of the scores of the underlying NUTS-2 regions. We only do this calculation for the core European logistics area : the Benelux, western German and northern French NUTS-1 regions. The full NUTS-1 ranking can be found in Attachment C with the same visual representation as for the NUTS-2 matrix ( strengths in green, weaknesses in red). The summary gives following result:

Costs Transport system Accessibility Supply Labour Know-how SCORE Ranking 2009 Ranking 2006

Weight % 21% 29% 29% 9% 9% 3%WALLONIE (B) 4.4 2.4 1.6 2.5 2.4 3.3 2.58 1 2

NORD - PAS-DE-CALAIS (F) 2.8 2.5 3.0 2.1 2.5 3.8 2.69 2 3

VLAANDEREN (B) 6.7 1.8 1.8 2.2 3.2 1.6 2.97 3 1

EST (F) 3.9 3.2 2.3 2.6 3.5 3.8 3.07 4 4

BRUSSELS HOOFDST. GEWEST (B) 8.7 1.8 1.6 3.6 0.8 2.3 3.22 5 5

NORDRHEIN-WESTFALEN (DL) 7.4 2.7 1.4 2.4 3.5 2.9 3.32 6 6

SAARLAND (DL) 5.6 3.5 2.6 1.5 2.5 3.3 3.37 7 8

RHEINLAND-PFALZ (DL) 6.0 3.5 1.3 2.3 4.7 3.2 3.38 8 9

ILE DE France (F) 6.4 1.9 4.0 3.3 1.5 2.8 3.54 9 10

BASSIN PARISIEN (F) 3.2 4.3 3.9 2.7 2.8 4.0 3.67 10 12

ZUID-NEDERLAND (NL) 8.8 2.3 1.5 2.3 7.4 2.0 3.83 11 7

OOST-NEDERLAND (NL) 6.6 3.3 2.5 2.5 7.4 2.8 4.03 12 11

WEST-NEDERLAND (NL) 9.0 2.2 2.9 3.1 6.8 2.1 4.30 13 13

LUXEMBOURG (GR. DUCHE) 10.2 3.4 1.8 3.2 3.5 2.5 4.31 14 15

NOORD-NEDERLAND (NL) 5.3 4.5 3.9 2.2 5.8 2.7 4.33 15 14 The rise of Wallonia and Nord-Pas-de-Calais was already clear in the 2006 report published by the Flanders Instiute for Logistics, but is now translated into a number 1 and 2 position for Wallonia and Nord-Pas-de-Calais; this means that Flanders steps back to 3rd position, reflecting the fact that some of its former top-regions like Antwerp suffer of lack of space and road congestion. Densely populated top regions for logistics tend to become victim of their own success somehow. This is also the case for the Dutch regions who historically played a major role in European logistics and distribution. NUTS-1 region Zuid-Nederland, that consists of the NUTS-2 regions Noord-Brabant and Limburg-NL, drops further from the 7th place in 2006 to 11th place now in 2009; the major reason for this drop are the real estate costs that have risen proportionally more than in surrounding regions. France has two NUTS-1 regions in the top-5: Nord-Pas-de-Calais and Est. Amongst the German “Länder” has Nordrhein-Westfalen the best score and 6th place, closely followed by two other German “Länder”: Saarland and Rheinland-Pfalz. In terms of local buying power, the German regions have the highest score of all regions studied.

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A look at the future A way of analysing the strengths and weaknesses, is to look at the way factors which have been taken into account will evolve in the future, according to the prognosis we can establish today. The indicators mentioned here above have to be actualised on a regular basis. The indicators which are most likely to change, and which in consequence have to be carefully followed, are: • Costs and offer in warehouse space and industrial land. The Flemish regions have some of the best scores in these categories, but the limited provision in some territories can make these prices go up in a relatively short period of time. If the governmental instances do not free enough land to be put on the market, a reverse effect might be generated. • The Transportation infrastructure and the accessibility to markets, and more specifically the following aspects: - road congestion - evolution in the transportation infrastructure and freight figures - accessibility to markets. Schürmann, Spiekermann and Wegener (University of Dortmund) specialised in that area, and have built several scenarios on the subject (Trans-European Transport Networks and Regional Economic Development 2002, http://ww.raumplanung.uni-dortmund.de/rwp/ersa2002/cd-rom/papers/174.pdf) for the period 1996-2016 (see below, loss and competitive advantage in terms of accessibility to markets). - The development of spending power, in Western Europe, but even more in the former countries of the Eastern block. Since these present a relatively important population with a still limited spending power, one should keep an eye on them as well. Once the spending power and the transport infrastructure in these region will have developed, the gravity point of the European market will shift towards to East. Ports like Hamburg, which are closer to the Eastern European population concentrations (like Poland), could take advantage of this situation. The evolution of economies such as Portugal, and the pace at which it reached the European standards can teach us much on how the former countries of the Eastern block are likely to evolve. According to Experian forecasts (http://www.business-strategies.co.uk/content.asp?ArticleID=603), one can expect that consumption per inhabitant in the Central European countries will reach Portugal’s current level in 2016. These countries will have then enjoyed the same evolution as Portugal did after its entry in the EU in 1986. The expected evolutions of data regarding population, employment and spending power are based on regional forecasts from Experian (http://www.business-strategies.co.uk/Content.asp?ArticleID=603). On this basis, and in combination with Schürmann, Spiekermann and Wegener’s scenarios regarding accessibility mentioned here above, matrixes were developed which give an idea of how ours will look like in 2020. Further on, in these forecasts, models developed by the EU ESPON “Transport services and networks” workgroup were widely taken into account. The following map gives an example of what the tonnages transported between the new EU countries and the EU-15 will look like in 2019:

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Source: ESPON (European Spatial Observation Network) Project 1.2.1., Transport services and networks: territorial trends and supply, Final report, September 2004, available electronically only at http://www.espon.eu/mmp/online/website/content/projects/259/652/file_2202/fr-1.2.2.-full.pdf

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Of course, the increasing aging of the European population mentioned here above (in regions like Northern Italy, Northern Spain, Eastern Germany and Eastern Europe in general) has been taken into account. South-Eastern England will be relatively less impacted by the aging of population, as a consequence of its constant level of economic migration, which secures a younger population structure than in most of the other European countries for several years. Expected increase of population 2000-2030:

Source: ESPON (European Spatial Observation Network) Project 1.2.1., Transport services and networks: territorial trends and supply, Final report, September 2004, available electronically only at http://www.espon.eu/mmp/online/website/content/projects/259/652/file_2202/fr-1.2.2.-full.pdf

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Expected increase of population 200-2030:

Source: ESPON (European Spatial Observation Network) Project 1.2.1., Transport services and networks: territorial trends and supply, Final Report, September 2004, alleen electronisch verkrijgbaar via http://www.espon.eu/mmp/online/website/content/projects/259/652/file_2202/fr-1.2.1-full.pdf

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In the forecast regarding the crucial element of “Available workforce”, the Experian forecasts on employment, and the latest current unemployment figures per NUTS-2 region published by Eurostat in early 2008 (2006 figures) were essentially taken into account. Unemployment figures per NUTS-2 region, 2006:

Source: Eurostat. It is also important to know that further prescriptions were used in these forecasts: • Offer of industrial land. Historically, and for decades, Flanders has presented lower real estate prices than most of the surrounding regions. The relatively wide offer in terms of land prescriptions is crucial for this matter. In Flanders, this subject is studied in the frame of the “Strategisch Plan Ruimtelijke Economie”. Results of the forecasts presented in this study show that a large portion of the future planning rounds will be devoted to the needs related to industrial land as assessed by the “Strategisch

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Plan Ruimtelijke Economie” for Flanders. A sufficient offer in terms of industrial land is necessary in order to control the evolution of rental and capital values. It is also important that the different players are given sufficient legal securities, also regarding permits and concessions. • Labour costs. On term, the current big differences between labour costs will widely decrease, under the influence of the EU development. Of course, differences will always remain between more and less urbanised regions. • Transportation system. Important works on capacity and infrastructure will be led according to the currently known scenarios, like TEN. This will be a priority in the process of removing the few flaws in the transportation system. • The further development of inter-modality will be of prior importance as well in order to meet the needs of the future transportation fluxes. It seems the currently planned concrete improvements regarding rail, air and ship transportation will be carried out within the foreseen timeframe.

Forecasted Matrix 2020 by NUTS-2 region. The full forecasted matrix 2020 can be found in Attachment D. The summary underneath shows Liège is forecasted to lose its number 1 position to Hainaut. Liège stays extremely well ranked on 3rd place but the limited availability of land give this region a slight disadvantage versus Hainaut who will be nr1 in our view. This reflects the growing importance of good transport infrastructure towards markets south of the actual core European logistics regions; the Seine-Nord Europe canal junction that will upgrade the inland waterway between the Paris region and the North of France and Belgium also increases the score of Nord-Pas-de-Calais and Hainaut. The gradual shift of the centre of gravity of the European markets towards central Europe will result in a rise of German regions like Köln and Düsseldorf; the good geographical position towards main German and central-European markets will keep Limburg (B) and Liège in a strong 2 and 3 position:

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Summary:

Forecast 2020 NUTS-2 regions CostsTransport

system Accessibility Supply LabourKnow-how SCORE

Forecasted Ranking 2020 Ranking 2009

Weight % 19% 27% 27% 8% 15% 3% TotaalHAINAUT (Charleroi) 5.9 2.4 1.8 1.0 2.3 3.0 2.8 1 3

LIMBURG -B (Genk-Hasselt) 5.9 2.2 1.7 1.3 3.3 1.7 2.9 2 2LIEGE 6.7 1.9 1.2 2.3 3.3 2.5 2.9 3 1

NORD - PAS-DE-CALAIS (Lille) 6.6 2.3 2.5 1.3 2.2 3.5 3.1 4 4DÜSSELDORF 9.3 1.8 0.9 3.3 2.3 2.5 3.2 5 14

KÖLN 10.3 1.7 0.8 3.3 2.3 2.5 3.3 6 11ALSACE (Strasbourg) 6.1 2.8 2.1 2.0 4.3 3.5 3.4 7 7

ARNSBERG 7.3 3.5 1.4 2.0 3.0 3.3 3.5 8 10VLAAMS BRABANT (Vilvoorde) 10.1 2.1 1.5 2.8 1.7 1.8 3.5 9 25

SAARLAND 6.7 3.6 2.1 1.7 2.8 3.3 3.5 10 18RHEINHESSEN-PFALZ (Kaiserslautern) 7.3 3.8 1.7 1.5 2.7 3.3 3.5 11 19

ANTWERPEN 7.9 1.8 2.4 2.8 3.8 1.0 3.5 12 9NAMUR 7.1 2.8 2.1 2.8 3.3 3.5 3.5 13 5

BRUSSELS CAP.REGION 10.5 1.9 1.7 4.0 1.0 2.3 3.5 14 15OOST-VLAANDEREN (Gent) 6.8 2.3 2.5 2.5 4.5 2.0 3.5 15 8BRABANT WALLON (Wavre) 9.6 2.5 1.6 2.8 1.8 3.3 3.5 16 13

KOBLENZ 8.3 2.9 0.9 2.5 4.3 3.0 3.6 17 12PICARDIE (Péronne) 6.1 2.8 3.2 1.3 3.7 4.0 3.6 18 20

Luxembourg - B( Arlon) 6.5 3.5 1.6 1.8 5.3 3.5 3.7 19 6LORRAINE ( Nancy ) 5.4 3.6 2.9 2.8 3.7 3.8 3.7 20 16WEST-VLAANDEREN 7.3 2.0 2.9 2.4 5.3 1.1 3.8 21 17LIMBURG -NL ( Venlo) 9.2 2.2 1.4 3.0 5.3 2.0 3.9 22 23

TRIER 7.9 3.6 1.4 2.8 4.7 3.3 3.9 23 21MUNSTER 8.3 3.6 2.3 2.3 3.0 3.3 3.9 24 27

OVERIJSSEL ( Enschede) 6.9 3.2 2.4 2.0 5.7 2.8 4.0 25 26ZEELAND (Terneuzen) 7.1 2.3 3.1 3.8 5.7 2.3 4.1 26 24

HAMBURG 10.9 1.7 4.2 3.0 0.8 2.3 4.1 27 41ILE DE France ( Paris) 10.6 2.0 3.1 3.8 2.3 2.8 4.2 28 22

FLEVOLAND (Lelystad) 7.6 3.2 3.3 2.0 4.8 2.8 4.2 29 30CHAMP.-ARDENNE (Reims) 5.4 4.6 3.0 2.5 5.3 4.0 4.2 30 29

DRENTHE (Emmen) 6.2 3.7 3.7 2.5 5.7 2.8 4.4 31 33BERLIN 9.6 2.7 4.4 3.3 1.7 3.0 4.4 32 42

GELDERLAND (Nijmegen) 7.8 3.3 2.3 2.5 7.0 2.8 4.4 33 35ZUID-HOLLAND (Rotterdam) 10.9 2.0 2.8 2.8 4.8 2.0 4.4 34 37

NOORD-BRABANT (Eindhoven) 9.9 2.5 2.1 2.5 6.7 2.0 4.4 35 31PRAHA (Prague) 6.8 3.0 4.6 2.4 5.3 2.8 4.4 36 28

DARMSTADT (Frankfurt) 13.3 2.3 1.7 5.6 2.1 2.8 4.5 37 34GRONINGEN REGION 6.5 4.6 3.8 2.0 5.3 2.5 4.6 38 38RHONE-ALPES (Lyon) 7.2 3.1 5.8 2.8 3.3 3.3 4.6 39 32

PROVENCE-ALPES COTE D'AZUR ( Marseille) 6.7 3.7 6.3 4.8 1.7 3.8 4.7 40 39FRIESLAND (Leeuwarden) 6.3 4.7 4.3 2.0 5.7 2.8 4.8 41 40

WIEN 9.8 2.7 4.9 2.9 3.6 3.8 4.8 42 47LUXEMBOURG (GRAND DUCHE) 13.3 3.3 1.9 3.5 3.7 2.5 4.9 43 36

UTRECHT REGION 11.8 2.6 2.7 3.5 5.8 2.0 4.9 44 44NOORD-HOLLAND (Amsterdam) 11.8 2.3 3.4 3.3 5.3 2.0 5.0 45 43

BRATISLAVSKY KRAJ 5.9 3.8 7.1 2.0 5.3 4.3 5.2 46 45WEST MIDLANDS (Birmingham) 11.2 2.9 4.0 3.4 5.7 2.5 5.2 47 55

OBERBAYERN (München) 11.6 3.1 4.7 4.0 3.4 2.8 5.3 48 51MAZOWIECKIE (Warszawa) 6.5 4.5 7.9 1.9 3.0 4.5 5.4 49 53

KOZEP-MAGYAR.(Budapest) 7.6 3.9 6.5 2.8 5.2 4.5 5.4 50 46SALZBURG 8.4 4.6 4.9 3.8 5.7 4.3 5.5 51 50

TIROL (Innsbruck) 9.3 4.5 4.8 5.0 5.8 4.3 5.7 52 54LOMBARDIA (Milano) 11.0 4.1 4.1 4.0 6.3 4.0 5.8 53 49

LAZIO (Roma) 9.8 4.1 5.6 4.0 5.2 5.0 5.8 54 52SYDSVERIGE (Malmö)/Öresund 10.0 4.7 6.8 4.0 3.8 3.0 6.0 55 58

SW SCOTLAND (Glasgow) 10.0 3.9 7.2 3.8 5.5 3.0 6.2 56 61GREATER LONDON 16.4 2.4 4.1 6.5 5.0 1.8 6.3 57 56

VASTSVERIGE (Göteborg) 10.0 5.3 7.3 4.3 5.8 3.0 6.7 58 48CATALUNA (Barcelona) 12.2 3.5 7.4 5.0 6.0 3.8 6.8 59 59LISBOA VALE DO TEJO 8.4 4.7 11.8 3.5 5.0 5.0 7.3 60 57

COM. DE MADRID 12.1 4.6 9.7 4.0 5.4 4.8 7.5 61 60median score 8.3 3.1 3.0 2.8 4.5 3.0 4.4

One of the major shifts in the 2020 forecasted matrix versus the actual situation is that most European markets will better accessible from a larger area. If we compare the Accessibility indixes of 2016, developed by Schürmann, Spiekermann en Wegener (Trans–European Transport Networks and Regional Economic Development, 2002, see above) with the actual ones, it strikes that several rather peripheral locations like Acquitaine/France or Schleswig-Holstein Northern Germany have accessibility scores that rank above the European average. This is due to the gradual development of the transport infrastructure towards regions that are remotely located versus the core EU; the EU supports this development very

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actively. The map ‘Accessibilty by road & rail in 2016’, following an average devopment scenario (TEN Scenario 10) will look as follows according to Schürmann, Spiekermann en Wegener in 2016: Accessibilty by road & rail in 2016: Source: Trans –European Transport Networks and Regional Economic Development, 2002, http://www.raumplanung.uni-dortmund.de/rwp/ersa2002/cd-rom/papers/174.pdf The map above indeed shows a much less concentrated picture than than the actual Accessibility maps. ( see ESPON,TRANSPORT SERVICES AND NETWORKS: TERRITORIAL TRENDS AND BASIC SUPPLY OF INFRASTRUCTURE FOR TERRITORIAL COHESION (2002-04), Third Interim Report, http://www.espon.lu/online/documentation/projects/thematic/1197/3.ir-1.2.1.pdf, pagina 166 and following)

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Regions like the Ruhrgebiet, North-East Belgium and the Southern part of the Netherlands will in part lose the competitive advantage of their good accessibility to major markets. On the other hand this means that this enlarged accessibility gives even better and quicker access to larger markets from the central locations, which might lead to more and bigger central EDC’s and proportionally less regional distribution centres.

Forecasted Ranking by NUTS-1 region. Like for the 2009 matrix we calculate the NUTS-1 2020 forecasted ranking by means of the average scores of the underlying NUTS-2 regions. The full detail of the NUTS-1 2020 forecasted ranking can be found in Attachment E. The averages for Wallonia and Flanders are slightly lower than the score of Nord-Pas-de-Calais. Nordrhein-Westfalen would rise from 6th to 4th place by 2020 because of its more and more central location and real estate prices that are expected to stay relatively stable. Northern French regions like Bassin Parisien , Picardie and to a lesser extent, Champagne-Ardenne are also expected to develop gradually as mature logistics locations.

Summary: Costs Transport system Accessibility Supply Labour Know-how SCORE Ranking 2020 Ranking 2009 Ranking 2006Weight % 19% 27% 27% 8% 15% 3%

NORD - PAS-DE-CALAIS (F) 6.6 2.3 2.5 1.3 2.2 3.5 3.09 1 2 3

WALLONIE (B) 7.0 2.6 1.6 2.1 3.2 3.2 3.26 2 1 2

VLAANDEREN (B) 7.6 2.1 2.2 2.3 3.7 1.5 3.41 3 3 1

NORDRHEIN-WESTFALEN (DL) 8.8 2.6 1.3 2.7 2.7 2.9 3.46 4 6 6

SAARLAND (DL) 6.7 3.6 2.1 1.7 2.8 3.3 3.48 5 7 8

BRUSSELS HOOFDST. GEWEST (B) 10.5 1.9 1.7 4.0 1.0 2.3 3.52 6 5 5

EST (F) 5.8 3.2 2.5 2.4 4.0 3.6 3.56 7 4 4

RHEINLAND-PFALZ (DL) 7.5 3.4 1.3 2.3 3.9 3.2 3.60 8 8 10

BASSIN PARISIEN (F) 5.7 3.7 3.1 2.1 4.5 4.0 3.93 9 10 12

ILE DE France (F) 10.6 2.0 3.1 3.8 2.3 2.8 4.16 10 9 9

ZUID-NEDERLAND (NL) 9.6 2.4 1.8 2.8 6.0 2.0 4.16 11 11 7

OOST-NEDERLAND (NL) 7.6 3.2 2.6 2.2 5.8 2.8 4.19 12 12 11

NOORD-NEDERLAND (NL) 6.3 4.3 3.9 2.2 5.6 2.7 4.57 13 15 14

WEST-NEDERLAND (NL) 10.5 2.3 3.0 3.3 5.4 2.1 4.61 14 13 13

LUXEMBOURG (GR. DUCHE) 13.3 3.3 1.9 3.5 3.7 2.5 4.87 15 14 15

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Attachments Attachment A: Glossary Ranked Matrix A table where geographical regions are measured in a few domains built under a certain amount of elements (columns). Each cell in the table is concordant with a geographical region and a certain element. The score is assigned according to the C&W methodology, depending on size and value. The total of the scores for a region allows a ranking of these regions. The Ranked Matrix is an excellent instrument when it comes to analysing the strengths and weaknesses of a region. Domain A category of macro-economic data which are used in the Ranked Matrix. Costs, Transportation system, Accessibility, Supply, Labour, Know-How. It gathers all the subjects which concern the sectors of logistics and distribution, and which can be quantified. Yet there are other interesting subjects which simply cannot be quantified, like the quantity and fluidity of permit deliverance, and other aspects of governmental administration (like tolls), taxation, rulings… Matrix-elements Specific macro-economic factors allowing the quantification of a specific domain. The absolute value of a region for a matrix-element is assigned according the C&W methodology. This allows the study of several regions, and their comparison with other EU regions following an identical methodology. In this study, 19 matrix-elements were retained, grouped under 6 different domains. Weight In the Ranked Matrix, weights which are used in order to assign a certain degree of importance to specific domains and matrix-elements. The total of weight percentages of all the matrix-elements of a domain is 100%, and allows the calculation of a subtotal of the scores per domain. The total weight percentages of all the domains is 100%, and allows the calculation of a total score per region. The corrected weights in this study are based on the experience of C&W in the field of location studies for logistics and distribution. Sensitivity An indication of the relation between the absolute data for a matrix-element, and the adapted score. This sensitivity gives the degree to which the absolute value must vary before the score does. The sensitivity is only rendered when the relation between absolute data and the score is sufficiently linear, and when the indicator is made of only one component. This sensitivity is also important when it comes to assess the stability of a specific ranking, for instance by observing how much the value of a region must drop before this region goes down in the Ranked Matrix. NUTS Means “Nomenclature d’Unités Territoriales Statistiques” carried out by Eurostat. This allows the definition of comparable regions across the borders of the EU countries. NUTS levels retained in this

26



study are: NUTS-1 (region), NUTS-2 (province). The NUTS-0 (country) and NUTS-3 (arrondissement) levels were not retained. The following site maps the different NUTS regions: http://ec.Europa.eu/comm/eurostat/ramon/NUTS/maps_searchpage_en.cfm).

-27-

Attachment B: Matrix Ranking by NUTS-2 region.

28



NUTS code N

UT

S2 R

EG

ION

(M

ajor

ci

ty)

Rent

s (€)

Land

Pric

es

Labo

ur C

osts

Subt

otal

Road

Den

sity

Road

Con

gesti

on

Rail

Den

sity

Road

Fre

ight

Rail

Freig

ht

Air

Freig

ht

Ship

ping

Fre

ight

Subt

otal

Buyin

g Po

wer

Acc

ess t

o EU

Cor

e

Acc

ess t

o Ce

ntra

l Eur

ope

Subt

otal

Ava

ilabl

e Uni

ts >

10,0

00

sq.m

Land

Sup

ply

Subt

otal

Ava

ilabl

e Lab

ourfo

rce

Labo

ur p

rodu

ctiv

ity

Subt

otal

Logi

stics

Edu

catio

n

Lang

uage

kno

wled

ge

Subt

otal

SCO

RE

Ran

king

200

9

Labour Know-how

Total

Costs Transport system Accessibility Supply

Weight 3.0 3.0 2.0 3.5 4.0 1.0 1.0 3.0 2.0 1.0 3.0 5.0 1.5 3.5 1.0 5.0 1.0 1.0 1.5 2.0 1.0 1.5 1.0 1.0 0.5

Weight % 38% 38% 25% 21% 27% 7% 7% 20% 13% 7% 20% 29% 25% 58% 17% 29% 50% 50% 9% 67% 33% 9% 50% 50% 3%

BE33 LIEGE 0.6 2.5 11.8 4.1 1.0 2.0 0.8 1.5 1.5 2.0 2.0 1.5 1.2 0.4 3.0 1.0 1.7 3.0 2.4 1.4 4.9 2.6 2.5 2.5 2.5 2.10 1BE22 LIMBURG -B (Genk-Hasselt) 0.6 1.6 11.0 3.6 1.0 2.0 2.0 2.0 2.0 3.5 3.0 2.0 1.8 0.5 3.0 1.2 1.5 0.8 1.2 2.0 4.6 2.9 2.5 0.9 1.7 2.10 2BE32 HAINAUT (Charleroi) 0.2 1.0 11.0 3.2 1.2 2.0 1.0 2.0 2.0 3.8 4.0 2.2 2.2 1.0 3.5 1.7 1.0 0.9 1.0 0.5 5.3 2.1 3.0 3.5 3.3 2.19 3FR30 NORD - PAS-DE-CALAIS (Lille) 2.8 0.2 6.5 2.8 2.2 4.0 1.0 2.5 1.8 4.5 2.5 2.5 5.8 1.6 3.8 3.0 3.0 1.2 2.1 0.6 6.3 2.5 2.5 5.0 3.8 2.69 4BE35 NAMUR 1.0 1.0 11.8 3.7 1.2 2.0 1.5 3.0 3.0 4.0 3.0 2.4 3.4 1.0 3.5 2.0 6.0 2.5 4.3 0.8 4.7 2.1 3.0 4.0 3.5 2.73 5BE34 Luxembourg - B( Arlon) 0.2 0.6 11.8 3.3 1.5 2.0 2.0 3.9 3.0 3.0 7.0 3.4 1.4 1.0 3.3 1.5 2.5 2.0 2.3 1.8 7.1 3.6 4.0 4.0 4.0 2.75 6FR42 ALSACE (Strasbourg) 4.0 1.6 6.5 3.7 3.0 2.5 1.7 3.0 2.0 4.0 3.0 2.8 5.4 0.5 2.8 2.1 3.5 2.0 2.8 3.8 3.8 3.8 2.5 5.0 3.8 2.90 7BE23 OOST-VLAANDEREN (Gent) 1.0 6.4 12.0 5.8 1.2 2.0 1.0 1.9 1.9 3.0 3.0 2.0 3.2 1.0 3.6 2.0 2.2 2.7 2.5 3.0 3.5 3.2 3.0 1.0 2.0 2.90 8BE21 ANTWERPEN 3.4 7.6 13.0 7.4 1.0 6.0 0.8 1.0 1.0 3.0 1.0 1.5 4.4 0.6 3.5 2.0 2.2 2.5 2.4 2.4 1.2 2.0 1.0 1.0 1.0 2.96 9DEA5 ARNSBERG 4.8 2.8 7.5 4.7 3.0 2.0 4.0 3.0 4.0 4.0 5.0 3.6 3.0 0.5 2.5 1.5 2.0 2.0 2.0 4.5 2.9 4.0 3.0 3.5 3.3 3.08 10DEA2 KÖLN 8.2 7.8 10.5 8.6 1.0 5.5 2.0 1.5 1.0 3.0 2.0 1.8 0.2 0.3 2.8 0.7 3.0 3.0 3.0 4.8 0.5 3.4 1.5 3.5 2.5 3.13 11DEB1 KOBLENZ 6.6 3.8 8.5 6.0 3.0 3.0 3.0 3.0 3.0 4.0 3.0 3.1 0.8 0.5 2.8 1.0 2.5 3.0 2.8 5.6 2.4 4.5 2.5 3.5 3.0 3.15 12BE31 BRABANT WALLON (Wavre) 5.4 6.4 13.5 7.8 1.0 4.0 1.0 1.5 2.0 3.0 4.0 2.2 1.6 1.0 3.5 1.6 2.1 3.0 2.6 1.5 1.5 1.5 3.5 3.0 3.3 3.16 13DEA1 DÜSSELDORF 10.4 8.4 10.5 9.7 0.5 6.5 2.0 1.5 1.0 2.0 2.0 1.7 0.4 0.3 2.8 0.7 3.0 2.0 2.5 3.7 0.7 2.7 1.5 3.5 2.5 3.22 14BE10 BRUSSELS CAP.REGION 4.8 9.8 13.0 8.7 0.5 7.0 1.0 1.5 2.0 1.8 2.0 1.8 2.8 0.5 3.5 1.6 3.2 3.9 3.6 0.9 0.6 0.8 1.5 3.0 2.3 3.22 15FR41 LORRAINE ( Nancy ) 5.4 1.0 6.5 4.0 3.0 3.0 2.0 3.0 3.0 6.0 5.0 3.5 7.2 0.5 3.0 2.6 3.0 2.0 2.5 2.2 5.1 3.2 2.5 5.0 3.8 3.24 16BE25 WEST-VLAANDEREN 3.0 7.0 11.5 6.6 1.0 2.0 2.5 1.8 2.0 4.5 1.5 1.8 5.0 1.2 3.8 2.6 1.9 2.4 2.2 4.9 4.0 4.6 1.2 0.9 1.1 3.28 17DEC0 SAARLAND 5.4 4.8 7.0 5.6 1.5 3.0 3.0 4.0 3.0 4.0 6.0 3.5 4.2 1.8 2.8 2.6 1.5 1.5 1.5 2.5 2.5 2.5 3.0 3.5 3.3 3.37 18DEB3 RHEINHESSEN-PFALZ (Kaiserslautern) 6.8 4.0 7.8 6.0 2.5 3.0 3.0 4.0 5.0 4.0 5.0 3.8 3.8 0.5 2.8 1.7 1.5 1.5 1.5 6.1 1.4 4.5 3.0 3.5 3.3 3.48 19FR22 PICARDIE (Amiens) 1.6 2.8 6.8 3.4 5.0 3.0 3.0 2.9 4.0 3.0 4.0 3.8 7.4 2.0 4.3 3.7 2.0 3.1 2.6 0.9 5.8 2.5 3.0 5.0 4.0 3.48 20DEB2 TRIER 7.6 2.0 9.5 6.0 3.0 2.0 4.0 4.0 5.0 4.0 4.0 3.7 2.4 0.5 3.0 1.4 2.5 3.0 2.8 5.7 3.3 4.9 3.0 3.5 3.3 3.51 21FR10 ILE DE France ( Paris) 7.4 4.6 7.5 6.4 0.5 9.5 1.0 1.0 1.5 1.0 3.0 1.9 8.2 2.0 4.5 4.0 2.2 4.3 3.3 2.0 0.4 1.5 1.5 4.0 2.8 3.54 22NL42 LIMBURG -NL ( Venlo) 6.8 8.0 11.5 8.4 2.5 2.5 1.9 1.4 1.4 3.0 2.5 2.1 0.6 0.3 3.5 0.9 1.8 3.0 2.4 6.5 9.3 7.4 1.5 2.5 2.0 3.54 23NL34 ZEELAND (Terneuzen) 3.4 2.4 11.0 4.9 2.5 3.0 3.0 2.0 2.0 2.0 2.0 2.3 6.0 1.5 3.8 3.0 3.0 3.0 3.0 6.1 10.0 7.4 2.0 2.5 2.3 3.55 24BE24 VLAAMS BRABANT (Vilvoorde) 8.0 9.4 13.5 9.9 1.0 6.5 0.9 1.5 2.0 2.0 2.0 1.9 2.0 0.5 3.5 1.4 2.1 3.2 2.7 4.8 0.8 3.5 3.5 1.0 2.3 3.60 25NL21 OVERIJSSEL ( Enschede) 1.6 6.8 11.0 5.9 5.0 2.5 4.0 2.0 2.5 5.0 3.0 3.4 4.8 1.0 3.0 2.3 2.0 2.5 2.3 6.6 9.5 7.6 3.0 2.5 2.8 3.84 26DEA3 MUNSTER 5.4 6.0 9.5 6.7 3.0 2.0 4.0 3.0 4.0 4.0 5.0 3.6 7.8 0.5 2.8 2.7 2.5 2.0 2.3 4.2 3.1 3.8 3.0 3.5 3.3 3.85 27FR21 CHAMP.-ARDENNE (Reims) 1.0 2.8 6.5 3.1 6.0 1.0 3.2 3.9 4.0 5.0 6.0 4.7 7.0 3.0 3.5 4.1 3.5 2.0 2.8 1.5 6.4 3.1 3.0 5.0 4.0 3.86 28CZ01 PRAHA (Prague) 4.0 2.8 5.5 3.9 4.0 2.5 3.0 4.4 3.8 3.8 4.0 3.9 10.6 3.5 1.0 4.9 2.0 2.0 2.0 1.6 4.2 2.5 4.0 3.0 3.5 3.87 29NL23 FLEVOLAND (Lelystad) 1.6 6.2 11.5 5.8 5.0 2.0 3.0 2.0 3.0 3.0 3.0 3.3 5.6 2.0 3.8 3.2 3.0 2.5 2.8 7.5 5.4 6.8 3.0 2.5 2.8 4.02 30NL41 NOORD-BRABANT (Eindhoven) 8.2 8.6 11.5 9.2 3.0 3.0 2.1 1.4 1.4 3.0 3.0 2.4 3.6 1.0 3.5 2.1 1.5 3.0 2.3 7.0 8.3 7.4 1.5 2.5 2.0 4.12 31FR71 RHONE-ALPES (Lyon) 4.8 0.8 6.8 3.8 4.0 5.0 2.0 2.5 3.0 3.0 3.5 3.3 9.6 4.5 4.5 5.8 2.0 2.8 2.4 4.1 4.3 4.2 2.0 5.0 3.5 4.14 32NL13 DRENTHE (Emmen) 1.6 5.8 11.0 5.5 5.0 2.5 4.0 3.0 4.0 5.0 3.0 3.8 6.6 2.5 3.0 3.6 4.0 1.0 2.5 4.8 10.5 6.7 3.0 2.5 2.8 4.22 33DE71 DARMSTADT (Frankfurt) 11.0 9.8 11.0 10.6 1.5 6.0 1.5 1.5 2.5 0.5 4.0 2.4 1.0 1.8 2.5 1.7 4.8 5.0 4.9 5.8 0.2 3.9 2.0 3.5 2.8 4.23 34NL22 GELDERLAND (Nijmegen) 6.8 7.4 11.5 8.2 4.5 3.0 3.0 2.0 2.5 4.0 3.0 3.2 2.6 1.5 3.3 2.1 2.0 3.0 2.5 7.5 8.8 7.9 3.0 2.5 2.8 4.24 35LU00 LUXEMBOURG (GRAND DUCHE) 8.2 10.4 13.0 10.2 3.0 3.5 3.0 3.0 3.0 3.0 5.0 3.4 4.0 0.5 3.0 1.8 3.0 3.4 3.2 5.2 0.1 3.5 3.0 2.0 2.5 4.31 36NL33 ZUID-HOLLAND (Rotterdam) 8.2 9.8 12.5 9.9 2.5 6.0 1.6 0.9 1.0 2.0 1.0 1.8 5.2 1.5 3.5 2.8 1.0 5.0 3.0 7.1 6.8 7.0 1.5 2.5 2.0 4.32 37NL11 GRONINGEN REGION 1.6 5.2 11.0 5.3 7.0 2.0 4.0 4.0 4.0 6.0 4.0 4.8 6.8 2.5 3.5 3.7 3.0 1.2 2.1 3.8 8.2 5.3 2.5 2.5 2.5 4.33 38FR82 PROVENCE-ALPES COTE D'AZUR ( Marseille) 3.8 0.4 6.8 3.3 6.0 4.0 4.0 3.9 4.0 3.0 2.0 4.0 10.0 5.0 5.0 6.3 3.5 4.8 4.2 1.1 3.6 1.9 3.0 5.0 4.0 4.36 39NL12 FRIESLAND (Leeuwarden) 1.6 4.8 11.0 5.2 5.0 2.5 6.0 5.0 5.0 6.0 4.0 4.8 7.6 3.0 3.5 4.2 3.0 1.1 2.1 3.1 9.7 5.3 3.0 2.5 2.8 4.44 40DE6 HAMBURG 11.0 9.0 10.5 10.1 1.0 6.5 2.0 1.5 1.5 3.0 1.0 1.7 8.8 3.2 2.0 4.4 2.5 2.7 2.6 4.3 0.3 3.0 1.0 3.5 2.3 4.44 41DE3 BERLIN 8.2 7.2 10.5 8.4 2.5 4.5 1.5 2.5 3.0 3.0 3.5 2.9 9.4 3.5 1.5 4.6 3.3 3.0 3.2 1.4 3.3 2.0 2.5 3.5 3.0 4.48 42NL32 NOORD-HOLLAND (Amsterdam) 8.2 10.6 12.5 10.2 2.0 7.0 2.0 1.5 2.0 1.3 2.0 2.2 6.2 2.0 3.8 3.3 2.0 5.0 3.5 6.8 4.3 6.0 1.5 2.5 2.0 4.61 43NL31 UTRECHT REGION 10.2 11.4 12.0 11.1 3.0 5.5 2.0 1.5 2.0 2.0 3.0 2.6 4.6 1.5 3.3 2.6 2.0 4.0 3.0 7.9 4.1 6.6 1.5 2.5 2.0 4.71 44UK73 WEST MIDLANDS (Birmingham) 10.6 8.8 12.0 10.3 2.0 4.5 2.0 2.5 2.0 3.0 5.0 2.9 6.4 2.5 5.0 3.9 3.0 3.0 3.0 4.2 6.0 4.8 2.0 3.0 2.5 4.88 45HU01 KOZEP-MAGYAR.(Budapest) 4.0 5.2 3.5 4.3 6.5 2.5 3.5 4.6 4.8 3.5 4.0 4.7 11.6 6.5 1.0 6.9 1.5 2.5 2.0 1.1 9.4 3.9 5.0 6.0 5.5 4.98 46AT13 WIEN 7.8 11.2 14.0 10.6 1.5 4.5 2.0 3.5 4.0 2.7 3.5 2.9 10.2 4.5 1.2 5.4 2.5 3.0 2.8 3.7 0.8 2.7 3.0 4.5 3.8 5.23 47SK01 BRATISLAVSKY KRAJ 3.0 2.8 3.8 3.1 7.0 1.5 5.9 6.0 5.5 5.0 7.0 6.0 10.4 8.0 1.0 7.4 2.0 1.5 1.8 0.9 12.0 4.6 5.0 6.0 5.5 5.32 48IT2 LOMBARDIA (Milano) 9.8 9.6 11.0 10.0 6.0 5.5 3.0 2.0 3.0 2.5 5.5 4.2 8.4 2.7 2.9 4.2 3.0 4.0 3.5 4.1 5.2 4.5 3.0 5.0 4.0 5.35 49

AT32 SALZBURG 4.6 10.6 9.5 8.1 3.5 2.5 4.0 4.5 5.0 5.0 7.0 4.7 9.2 4.5 1.4 5.2 3.5 4.0 3.8 4.8 3.0 4.2 3.5 5.0 4.3 5.38 50DE21 OBERBAYERN (München) 11.6 11.0 10.5 11.1 2.5 6.5 3.5 2.5 3.0 2.5 4.5 3.3 8.6 4.0 1.7 4.8 3.5 3.4 3.5 6.8 0.3 4.6 2.0 3.5 2.8 5.45 51IT6 LAZIO (Roma) 9.6 8.2 10.5 9.3 5.0 5.0 3.5 2.5 4.5 3.0 5.0 4.2 9.8 4.7 3.5 5.8 3.0 4.5 3.8 1.3 6.1 2.9 4.0 6.0 5.0 5.58 52

PL07 MAZOWIECKIE (Warszawa) 8.2 2.2 3.0 4.7 7.0 3.0 3.9 4.4 4.2 3.5 6.0 5.2 12.2 8.5 1.0 8.2 1.8 1.8 1.8 0.8 12.0 4.5 5.0 7.0 6.0 5.63 53AT33 TIROL (Innsbruck) 5.4 12.2 9.5 9.0 3.0 3.0 4.0 4.5 5.0 5.0 7.0 4.6 9.0 4.5 1.5 5.1 5.0 5.0 5.0 4.9 3.4 4.4 3.5 5.0 4.3 5.65 54

UKI1&2 GREATER LONDON 12.2 11.8 12.5 12.1 3.0 11.0 1.3 1.5 1.4 1.0 2.0 2.6 8.0 2.0 4.5 3.9 5.0 7.0 6.0 8.6 5.5 7.6 1.5 2.5 2.0 5.66 55UKM3 SW SCOTLAND (Glasgow) 10.6 5.2 11.5 8.8 3.5 2.5 5.5 4.3 5.3 4.5 4.0 4.1 11.4 5.0 8.0 7.1 3.0 4.0 3.5 3.3 7.0 4.5 3.0 3.0 3.0 5.91 56

E04+Cop.D SYDSVERIGE (Malmö)/Öresund 11.4 4.0 13.0 9.0 8.0 2.0 5.0 4.5 3.5 3.5 3.0 4.8 11.0 6.0 3.0 6.8 3.5 4.5 4.0 2.4 8.2 4.3 3.5 2.5 3.0 6.08 57SE05 VASTSVERIGE (Göteborg) 9.8 4.0 13.0 8.4 8.5 2.0 4.0 5.5 5.5 5.5 3.0 5.5 11.8 6.5 3.5 7.3 4.0 4.5 4.3 2.3 7.8 4.1 3.5 2.5 3.0 6.32 58ES51 CATALUNA (Barcelona) 11.8 11.6 6.5 10.4 4.0 5.5 5.5 3.5 4.8 3.5 2.5 3.9 11.2 6.5 7.0 7.8 4.2 5.0 4.6 0.4 8.7 3.2 3.0 5.0 4.0 6.36 59ES3 COM. DE MADRID 12.0 12.0 6.8 10.7 3.5 4.0 3.8 4.5 5.0 3.0 8.0 4.8 10.8 10.0 9.0 10.0 3.5 4.0 3.8 0.4 6.9 2.6 4.0 6.0 5.0 7.28 60PT13 LISBOA VALE DO TEJO 5.4 9.0 5.7 6.8 6.5 4.0 6.0 4.2 5.8 4.0 3.0 4.9 12.0 12.0 12.0 12.0 3.0 4.0 3.5 1.8 11.5 5.0 5.0 6.0 5.5 7.28 61

median score 5.4 6.2 11.0 6.7 3.0 3.0 3.0 2.5 3.0 3.0 3.0 3.3 6.2 1.8 3.3 3.0 2.5 3.0 2.8 3.7 4.7 3.9 3.0 3.5 3.0 4.12

Labour Know-how

Total


-29-

Attachment C: Matrix Ranking by NUTS-1 region.

NU

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Labour Know-howTotaal


Weight 3.0 3.0 2.0 3.5 4.0 1.0 1.0 3.0 2.0 1.0 3.0 5.0 1.5 3.5 1.0 5.0 1.0 1.0 1.5 2.0 1.0 1.5 1.0 1.0 0.5Weight % 38% 38% 25% 21% 27% 7% 7% 20% 13% 7% 20% 29% 25% 58% 17% 29% 50% 50% 9% 67% 33% 9% 50% 50% 3%

WALLONIE (B) 1.5 2.3 12.0 4.4 1.2 2.4 1.3 2.4 2.3 3.2 4.0 2.4 2.0 0.9 3.4 1.6 2.7 2.3 2.5 1.2 4.7 2.4 3.2 3.4 3.3 2.58 1

NORD - PAS-DE-CALAIS (F) 2.8 0.2 6.5 2.8 2.2 4.0 1.0 2.5 1.8 4.5 2.5 2.5 5.8 1.6 3.8 3.0 3.0 1.2 2.1 0.6 6.3 2.5 2.5 5.0 3.8 2.69 2

VLAANDEREN (B) 3.2 6.4 12.2 6.7 1.0 3.7 1.4 1.6 1.8 3.2 2.1 1.8 3.3 0.8 3.5 1.8 2.0 2.3 2.2 3.4 2.8 3.2 2.2 1.0 1.6 2.97 3

EST (F) 4.7 1.3 6.5 3.9 3.0 2.8 1.9 3.0 2.5 5.0 4.0 3.2 6.3 0.5 2.9 2.3 3.3 2.0 2.6 3.0 4.4 3.5 2.5 5.0 3.8 3.07 4

BRUSSELS HOOFDST. GEWEST (B) 4.8 9.8 13.0 8.7 0.5 7.0 1.0 1.5 2.0 1.8 2.0 1.8 2.8 0.5 3.5 1.6 3.2 3.9 3.6 0.9 0.6 0.8 1.5 3.0 2.3 3.22 5

NORDRHEIN-WESTFALEN (DL) 7.2 6.3 9.5 7.4 1.9 4.0 3.0 2.3 2.5 3.3 3.5 2.7 2.9 0.4 2.7 1.4 2.6 2.3 2.4 4.3 1.8 3.5 2.3 3.5 2.9 3.32 6

SAARLAND (DL) 5.4 4.8 7.0 5.6 1.5 3.0 3.0 4.0 3.0 4.0 6.0 3.5 4.2 1.8 2.8 2.6 1.5 1.5 1.5 2.5 2.5 2.5 3.0 3.5 3.3 3.37 7

RHEINLAND-PFALZ (DL) 7.0 3.3 8.6 6.0 2.8 2.7 3.3 3.7 4.3 4.0 4.0 3.5 2.3 0.5 2.8 1.3 2.2 2.5 2.3 5.8 2.4 4.7 2.8 3.5 3.2 3.38 8

ILE DE France (F) 7.4 4.6 7.5 6.4 0.5 9.5 1.0 1.0 1.5 1.0 3.0 1.9 8.2 2.0 4.5 4.0 2.2 4.3 3.3 2.0 0.4 1.5 1.5 4.0 2.8 3.54 9

BASSIN PARISIEN (F) 1.3 2.8 6.7 3.2 5.5 2.0 3.1 3.4 4.0 4.0 5.0 4.3 7.2 2.5 3.9 3.9 2.8 2.6 2.7 1.2 6.1 2.8 3.0 5.0 4.0 3.67 10

ZUID-NEDERLAND (NL) 7.5 8.3 11.5 8.8 2.8 2.8 2.0 1.4 1.4 3.0 2.8 2.3 2.1 0.6 3.5 1.5 1.7 3.0 2.3 6.8 8.8 7.4 1.5 2.5 2.0 3.83 11

OOST-NEDERLAND (NL) 3.3 6.8 11.3 6.6 4.8 2.5 3.3 2.0 2.7 4.0 3.0 3.3 4.3 1.5 3.3 2.5 2.3 2.7 2.5 7.2 7.9 7.4 3.0 2.5 2.8 4.03 12

WEST-NEDERLAND (NL) 7.5 8.6 12.0 9.0 2.5 5.4 2.2 1.5 1.8 1.8 2.0 2.2 5.5 1.6 3.6 2.9 2.0 4.3 3.1 7.0 6.3 6.8 1.6 2.5 2.1 4.30 13

LUXEMBOURG (GR. DUCHE) 8.2 10.4 13.0 10.2 3.0 3.5 3.0 3.0 3.0 3.0 5.0 3.4 4.0 0.5 3.0 1.8 3.0 3.4 3.2 5.2 0.1 3.5 3.0 2.0 2.5 4.31 14

NOORD-NEDERLAND (NL) 1.6 5.3 11.0 5.3 5.7 2.3 4.7 4.0 4.3 5.7 3.7 4.5 7.0 2.7 3.3 3.9 3.3 1.1 2.2 3.9 9.5 5.8 2.8 2.5 2.7 4.33 15

mediaan 4.8 5.3 11.0 6.4 2.5 3.0 2.2 2.4 2.5 3.3 3.5 2.7 4.2 0.9 3.4 2.3 2.6 2.5 2.5 3.4 4.4 3.5 2.5 3.4 2.8 3.38

Labour Know-howTotaal


30



Attachment D: Forecasted Matrix NUTS-2 regions 2020.

31



NUTS code REGION 2020 FORECAST Re

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Costs Transport system Accessibility Supply Labour Know-howTotaal

Weight 3.0 3.0 2.0 3.5 4.0 1.0 1.0 3.0 2.0 1.0 3.0 5.0 1.5 3.5 1.0 5.0 1.0 1.0 1.5 2.0 1.0 2.8 1.0 1.0 0.5Weight % 38% 38% 25% 19% 27% 7% 7% 20% 13% 7% 20% 27% 25% 58% 17% 27% 50% 50% 8% 67% 33% 15% 50% 50% 3%

BE32 HAINAUT (Charleroi) 2.0 7.0 10.0 5.9 2.0 2.5 1.0 2.0 2.0 3.0 4.0 2.4 2.6 1.0 3.5 1.8 1.0 1.0 1.0 1.0 5.0 2.3 2.5 3.5 3.0 2.81 1 3BE22 LIMBURG -B (Genk-Hasselt) 2.0 7.0 10.0 5.9 2.0 2.5 1.5 2.0 1.5 3.5 3.0 2.2 2.5 1.0 3.0 1.7 1.5 1.0 1.3 3.0 4.0 3.3 2.5 0.8 1.7 2.86 2 2BE33 LIEGE 2.5 8.0 11.0 6.7 2.0 3.0 1.0 1.5 1.5 2.5 2.0 1.9 1.5 0.5 3.0 1.2 2.0 2.5 2.3 3.0 4.0 3.3 2.5 2.5 2.5 2.87 3 1FR30 NORD - PAS-DE-CALAIS (Lille) 4.0 7.5 9.0 6.6 2.5 4.0 1.0 2.0 1.5 4.0 2.3 2.3 6.2 0.5 3.8 2.5 1.0 1.5 1.3 1.0 4.5 2.2 2.0 5.0 3.5 3.09 4 4DEA1 DÜSSELDORF 8.0 10.0 10.0 9.3 1.0 8.0 1.5 1.0 1.0 2.0 2.0 1.8 0.4 0.5 2.8 0.9 3.0 3.5 3.3 3.0 1.0 2.3 1.5 3.5 2.5 3.18 5 14DEA2 KÖLN 9.0 11.0 11.0 10.3 1.0 7.0 1.0 1.0 1.0 2.0 2.0 1.7 0.2 0.5 2.5 0.8 3.0 3.5 3.3 3.0 1.0 2.3 1.5 3.5 2.5 3.31 6 11FR42 ALSACE (Strasbourg) 3.5 7.5 8.0 6.1 3.0 3.0 2.0 2.5 2.0 4.0 3.0 2.8 5.4 0.5 2.8 2.1 2.0 2.0 2.0 5.0 3.0 4.3 2.0 5.0 3.5 3.42 7 7DEA5 ARNSBERG 6.5 7.0 9.0 7.3 3.0 2.0 3.0 3.0 4.0 4.0 5.0 3.5 3.0 0.5 2.0 1.4 2.0 2.0 2.0 3.0 3.0 3.0 3.0 3.5 3.3 3.45 8 10BE24 VLAAMS BRABANT (Vilvoorde) 8.0 11.0 12.0 10.1 1.5 8.0 1.0 1.5 1.5 2.0 2.5 2.1 2.5 0.5 3.5 1.5 2.0 3.5 2.8 2.0 1.0 1.7 2.5 1.0 1.8 3.46 9 25DEC0 SAARLAND 6.5 6.0 8.0 6.7 2.0 3.0 3.0 4.0 2.5 5.0 6.0 3.6 4.2 1.0 2.5 2.1 1.5 1.8 1.7 3.0 2.5 2.8 3.0 3.5 3.3 3.48 10 18DEB3 RHEINHESSEN-PFALZ (Kaiserslautern) 7.0 7.0 8.0 7.3 2.5 3.5 2.5 4.0 5.0 4.0 5.0 3.8 3.8 0.5 2.5 1.7 1.5 1.5 1.5 3.0 2.0 2.7 3.0 3.5 3.3 3.50 11 19BE21 ANTWERPEN 4.0 9.0 12.0 7.9 1.9 8.0 1.0 1.0 1.0 3.0 1.0 1.8 4.9 1.0 3.5 2.4 2.0 3.5 2.8 5.0 1.5 3.8 1.0 1.0 1.0 3.50 12 9BE35 NAMUR 3.5 8.0 11.0 7.1 2.0 2.5 1.5 3.0 3.0 4.0 3.5 2.8 3.6 1.0 3.5 2.1 3.0 2.5 2.8 3.0 4.0 3.3 3.0 4.0 3.5 3.50 13 5BE10 BRUSSELS CAP.REGION 8.0 12.0 12.0 10.5 1.0 8.0 1.0 1.5 1.5 2.0 2.0 1.9 3.1 0.5 3.5 1.7 3.0 5.0 4.0 1.0 1.0 1.0 1.5 3.0 2.3 3.52 14 15BE23 OOST-VLAANDEREN (Gent) 3.2 7.5 11.0 6.8 2.0 2.5 1.5 2.0 2.0 3.0 3.0 2.3 3.9 1.5 3.6 2.5 2.0 3.0 2.5 5.0 3.5 4.5 3.0 1.0 2.0 3.53 15 8BE31 BRABANT WALLON (Wavre) 7.0 10.5 12.0 9.6 1.5 6.5 1.0 1.5 2.0 3.0 4.0 2.5 1.9 1.0 3.5 1.6 2.0 3.5 2.8 2.0 1.5 1.8 3.5 3.0 3.3 3.55 16 13DEB1 KOBLENZ 7.0 9.0 9.0 8.3 3.0 4.0 2.5 2.5 2.0 4.0 3.0 2.9 0.8 0.5 2.5 0.9 2.0 3.0 2.5 5.0 3.0 4.3 2.5 3.5 3.0 3.56 17 12FR22 PICARDIE (Péronne) 3.5 7.0 8.5 6.1 3.0 3.0 2.5 2.0 3.5 3.0 3.0 2.8 7.6 1.0 4.3 3.2 1.5 1.0 1.3 3.0 5.0 3.7 3.0 5.0 4.0 3.58 18 20BE34 Luxembourg - B( Arlon) 3.0 7.0 11.0 6.5 2.0 3.0 2.0 3.0 3.0 3.0 7.0 3.5 1.7 1.0 3.3 1.6 2.5 1.0 1.8 5.0 6.0 5.3 3.0 4.0 3.5 3.67 19 6FR41 LORRAINE ( Nancy ) 3.0 6.0 8.0 5.4 3.0 4.5 1.5 3.0 3.0 6.0 5.0 3.6 7.3 1.0 3.0 2.9 3.6 2.0 2.8 3.0 5.0 3.7 2.5 5.0 3.8 3.70 20 16BE25 WEST-VLAANDEREN 3.5 8.5 11.0 7.3 2.0 2.5 2.0 1.8 2.0 4.0 1.5 2.0 5.6 1.5 3.8 2.9 1.8 3.0 2.4 6.0 4.0 5.3 1.2 0.9 1.1 3.77 21 17NL42 LIMBURG -NL ( Venlo) 8.5 9.0 10.5 9.2 2.5 4.0 1.5 1.5 1.5 3.0 2.5 2.2 1.1 1.0 3.5 1.4 2.0 4.0 3.0 5.0 6.0 5.3 1.5 2.5 2.0 3.88 22 23DEB2 TRIER 7.5 7.0 10.0 7.9 2.5 3.0 4.0 3.5 5.0 4.0 4.0 3.6 2.6 0.5 3.0 1.4 2.5 3.0 2.8 5.0 4.0 4.7 3.0 3.5 3.3 3.91 23 21DEA3 MUNSTER 7.0 9.0 9.0 8.3 3.0 3.0 4.0 2.5 4.0 4.0 5.0 3.6 6.4 0.5 2.5 2.3 2.5 2.0 2.3 3.0 3.0 3.0 3.0 3.5 3.3 3.92 24 27NL21 OVERIJSSEL ( Enschede) 3.5 8.0 10.5 6.9 4.0 3.0 4.0 2.0 2.5 5.0 3.0 3.2 5.1 1.0 3.0 2.4 2.0 2.0 2.0 5.0 7.0 5.7 3.0 2.5 2.8 3.95 25 26NL34 ZEELAND (Terneuzen) 4.0 8.0 10.5 7.1 2.5 3.5 3.0 2.0 2.0 2.0 2.0 2.3 6.4 1.5 3.8 3.1 4.5 3.0 3.8 5.0 7.0 5.7 2.0 2.5 2.3 4.07 26 24DE6 HAMBURG 11.0 11.0 10.5 10.9 1.0 8.0 1.5 1.3 1.3 2.8 1.0 1.7 8.1 3.2 1.8 4.2 2.5 3.5 3.0 1.0 0.3 0.8 1.0 3.5 2.3 4.12 27 41FR10 ILE DE France ( Paris) 9.5 12.0 10.0 10.6 1.0 11.0 1.0 1.0 1.2 1.0 2.5 2.0 8.4 0.5 4.5 3.1 2.0 5.5 3.8 3.0 1.0 2.3 1.5 4.0 2.8 4.16 28 22NL23 FLEVOLAND (Lelystad) 4.5 8.0 11.5 7.6 4.5 2.5 3.0 2.0 3.0 3.0 3.0 3.2 6.0 2.0 3.8 3.3 3.0 1.0 2.0 5.0 4.3 4.8 3.0 2.5 2.8 4.18 29 30FR21 CHAMP.-ARDENNE (Reims) 3.0 6.0 8.0 5.4 4.8 1.5 4.0 5.0 3.5 5.0 6.0 4.6 7.3 1.0 3.5 3.0 3.0 2.0 2.5 5.0 6.0 5.3 3.0 5.0 4.0 4.25 30 29NL13 DRENTHE (Emmen) 2.5 7.0 10.5 6.2 4.5 3.0 4.0 3.0 4.0 5.0 3.0 3.7 6.8 2.5 3.0 3.7 4.0 1.0 2.5 5.0 7.0 5.7 3.0 2.5 2.8 4.35 31 33DE3 BERLIN 9.0 9.5 10.5 9.6 2.1 6.0 1.5 2.0 2.8 2.8 3.4 2.7 8.5 3.5 1.2 4.4 3.3 3.2 3.3 1.0 3.0 1.7 2.5 3.5 3.0 4.36 32 42NL22 GELDERLAND (Nijmegen) 5.0 8.5 11.0 7.8 4.5 4.0 3.0 2.0 2.5 4.0 3.0 3.3 3.5 1.5 3.3 2.3 2.0 3.0 2.5 7.0 7.0 7.0 3.0 2.5 2.8 4.36 33 35NL33 ZUID-HOLLAND (Rotterdam) 10.0 11.0 12.0 10.9 2.5 8.0 1.5 1.0 1.0 2.0 1.0 2.0 5.5 1.5 3.5 2.8 1.0 4.5 2.8 5.0 4.5 4.8 1.5 2.5 2.0 4.41 34 37NL41 NOORD-BRABANT (Eindhoven) 9.0 10.0 11.0 9.9 3.0 3.5 2.5 1.5 1.5 3.0 3.0 2.5 3.6 1.0 3.5 2.1 1.5 3.5 2.5 7.0 6.0 6.7 1.5 2.5 2.0 4.42 35 31CZ01 PRAHA (Prague) 5.5 7.5 7.5 6.8 2.8 3.5 2.5 3.0 3.0 2.6 3.5 3.0 9.9 3.5 0.5 4.6 2.0 2.7 2.4 6.0 3.8 5.3 3.0 2.5 2.8 4.45 36 28DE71 DARMSTADT (Frankfurt) 11.5 17.0 10.5 13.3 1.5 7.5 1.3 1.2 2.4 0.5 3.5 2.3 0.8 1.8 2.4 1.7 5.2 6.0 5.6 3.0 0.2 2.1 2.0 3.5 2.8 4.47 37 34NL11 GRONINGEN REGION 2.5 7.5 11.0 6.5 6.0 2.5 4.0 4.0 4.0 6.0 4.0 4.6 7.2 2.5 3.5 3.8 3.0 1.0 2.0 5.0 6.0 5.3 2.5 2.5 2.5 4.59 38 38FR71 RHONE-ALPES (Lyon) 5.0 8.5 8.5 7.2 3.6 6.5 1.5 1.9 2.8 2.7 3.3 3.1 9.5 4.5 4.5 5.8 2.0 3.5 2.8 3.0 4.0 3.3 2.0 4.5 3.3 4.61 39 32FR82 PROVENCE-ALPES COTE D'AZUR ( Marsei 4.5 7.0 9.5 6.7 5.5 5.0 3.0 3.0 3.6 2.7 2.0 3.7 10.0 5.0 5.0 6.3 3.5 6.0 4.8 1.0 3.2 1.7 3.0 4.5 3.8 4.74 40 39NL12 FRIESLAND (Leeuwarden) 2.0 7.5 11.0 6.3 4.5 3.0 6.0 5.0 5.0 6.0 4.0 4.7 7.8 3.0 3.5 4.3 3.0 1.0 2.0 5.0 7.0 5.7 3.0 2.5 2.8 4.76 41 40AT13 WIEN 7.0 12.5 10.0 9.8 1.5 5.0 2.0 2.9 3.3 2.7 3.0 2.7 8.5 4.5 0.8 4.9 2.5 3.3 2.9 5.0 0.8 3.6 3.0 4.5 3.8 4.83 42 47LU00 LUXEMBOURG (GRAND DUCHE) 12.0 14.0 14.0 13.3 2.5 5.5 2.5 3.0 2.5 3.0 5.0 3.3 4.3 0.5 3.0 1.9 3.0 4.0 3.5 5.0 1.0 3.7 3.0 2.0 2.5 4.87 43 36NL31 UTRECHT REGION 11.0 12.5 12.0 11.8 3.0 6.0 2.0 1.5 2.0 2.0 3.0 2.6 5.0 1.5 3.3 2.7 2.0 5.0 3.5 7.0 3.5 5.8 1.5 2.5 2.0 4.94 44 44NL32 NOORD-HOLLAND (Amsterdam) 11.0 12.5 12.0 11.8 2.0 9.0 1.5 1.5 2.0 1.5 2.0 2.3 6.5 2.0 3.8 3.4 2.0 4.5 3.3 7.0 1.9 5.3 1.5 2.5 2.0 4.95 45 43SK01 BRATISLAVSKY KRAJ 5.0 7.0 5.5 5.9 4.5 3.0 4.0 3.5 3.2 4.0 3.6 3.8 9.0 8.0 1.0 7.1 2.0 1.9 2.0 4.0 8.0 5.3 4.0 4.5 4.3 5.18 46 45UK73 WEST MIDLANDS (Birmingham) 12.0 9.9 12.0 11.2 2.0 5.0 2.0 2.2 2.0 2.8 5.0 2.9 6.8 2.5 5.0 4.0 3.0 3.7 3.4 6.0 5.0 5.7 2.0 3.0 2.5 5.24 47 55DE21 OBERBAYERN (München) 11.5 12.5 10.5 11.6 2.3 6.8 3.0 2.0 2.8 2.5 4.5 3.1 8.2 4.0 1.7 4.7 3.5 4.4 4.0 5.0 0.3 3.4 2.0 3.5 2.8 5.27 48 51PL07 MAZOWIECKIE (Warszawa) 6.0 7.0 6.5 6.5 5.0 4.5 3.5 3.7 3.7 3.0 6.0 4.5 11.0 8.5 1.0 7.9 2.0 1.8 1.9 0.5 8.0 3.0 4.0 5.0 4.5 5.36 49 53HU01 KOZEP-MAGYAR.(Budapest) 8.0 8.0 6.5 7.6 5.5 3.4 3.0 3.2 3.4 3.1 3.5 3.9 10.3 6.5 0.5 6.5 2.0 3.5 2.8 4.0 7.5 5.2 4.0 5.0 4.5 5.42 50 46AT32 SALZBURG 5.0 11.0 9.5 8.4 3.5 3.0 3.5 4.3 4.7 5.0 7.0 4.6 8.0 4.5 1.4 4.9 3.5 4.0 3.8 7.0 3.0 5.7 3.5 5.0 4.3 5.47 51 50AT33 TIROL (Innsbruck) 5.5 13.0 9.5 9.3 3.0 3.5 3.5 4.2 4.7 5.0 7.0 4.5 7.6 4.5 1.5 4.8 5.0 5.0 5.0 7.0 3.4 5.8 3.5 5.0 4.3 5.72 52 54IT2 LOMBARDIA (Milano) 10.0 12.0 11.0 11.0 5.8 6.5 2.5 1.8 2.8 2.5 5.5 4.1 8.3 2.7 2.8 4.1 3.0 5.0 4.0 7.0 5.0 6.3 3.0 5.0 4.0 5.77 53 49IT6 LAZIO (Roma) 10.0 9.0 10.5 9.8 4.5 6.0 3.5 2.4 4.3 3.0 5.0 4.1 9.2 4.7 3.5 5.6 3.0 5.0 4.0 5.0 5.5 5.2 4.0 6.0 5.0 5.77 54 52

E04+Cop.D SYDSVERIGE (Malmö)/Öresund 11.0 6.7 13.5 10.0 8.0 2.0 5.0 4.2 3.5 3.5 2.8 4.7 11.0 6.0 3.0 6.8 3.5 4.5 4.0 2.0 7.5 3.8 3.5 2.5 3.0 6.04 55 58UKM3 SW SCOTLAND (Glasgow) 12.0 7.0 11.5 10.0 3.5 2.5 4.5 4.2 5.2 4.5 3.5 3.9 11.8 5.0 8.0 7.2 3.5 4.0 3.8 5.0 6.5 5.5 3.0 3.0 3.0 6.19 56 61

UKI1&2 GREATER LONDON 15.0 19.0 14.5 16.4 2.5 12.5 1.0 1.2 1.3 0.8 2.0 2.4 8.6 2.0 4.5 4.1 5.0 8.0 6.5 5.0 5.0 5.0 1.5 2.0 1.8 6.25 57 56SE05 VASTSVERIGE (Göteborg) 11.0 6.7 13.5 10.0 8.5 2.0 4.0 5.0 5.3 5.5 3.0 5.3 11.8 6.5 3.5 7.3 4.0 4.5 4.3 5.0 7.5 5.8 3.5 2.5 3.0 6.70 58 48ES51 CATALUNA (Barcelona) 12.5 15.0 7.5 12.2 3.5 6.5 5.0 3.1 4.0 3.2 2.3 3.5 9.9 6.5 7.0 7.4 4.5 5.5 5.0 5.0 7.9 6.0 3.0 4.5 3.8 6.75 59 59PT13 LISBOA VALE DO TEJO 7.3 11.0 6.2 8.4 6.0 4.8 5.5 4.0 6.0 3.6 3.0 4.7 11.0 12.0 12.0 11.8 3.0 4.0 3.5 3.0 9.0 5.0 5.0 5.0 5.0 7.30 60 57ES3 COM. DE MADRID 11.5 16.0 7.0 12.1 3.0 5.5 4.0 3.8 5.0 2.8 8.0 4.6 9.5 10.0 9.0 9.7 3.5 4.5 4.0 5.0 6.2 5.4 4.0 5.5 4.8 7.51 61 60

median score 7.0 8.5 10.5 8.3 2.8 4.0 2.5 2.2 2.8 3.0 3.0 3.1 6.4 1.5 3.3 3.0 2.5 3.5 2.8 5.0 4.0 4.5 3.0 3.5 3.0 4.35

Costs Transport system Accessibility Supply Labour Know-howTotaal

-32-

Attachment E: Forecasted Matrix NUTS-1 regio’ s 2020.

NUTS1 2020 FORECAST Rent

s

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9

Labour Know-howTotal


Weight 3.0 3.0 2.0 3.5 4.0 1.0 1.0 3.0 2.0 1.0 3.0 5.0 1.5 3.5 1.0 5.0 1.0 1.0 1.5 2.0 1.0 2.8 1.0 1.0 0.5Weight % 38% 38% 25% 19% 27% 7% 7% 20% 13% 7% 20% 27% 25% 58% 17% 27% 50% 50% 8% 67% 33% 15% 50% 50% 3%

NORD - PAS-DE-CALAIS (F) 4.0 7.5 9.0 6.6 2.5 4.0 1.0 2.0 1.5 4.0 2.3 2.3 6.2 0.5 3.8 2.5 1.0 1.5 1.3 1.0 4.5 2.2 2.0 5.0 3.5 3.09 1 2

WALLONIE (B) 3.2 8.1 11.0 7.0 1.9 3.5 1.3 2.4 2.3 3.1 4.0 2.6 2.3 0.9 3.4 1.6 2.1 2.1 2.1 2.8 4.1 3.2 2.9 3.4 3.2 3.26 2 1

VLAANDEREN (B) 4.1 8.6 11.2 7.6 1.9 4.7 1.4 1.7 1.6 3.1 2.1 2.1 3.9 1.1 3.5 2.2 1.8 2.8 2.3 4.2 2.8 3.7 2.0 0.9 1.5 3.41 3 3

NORDRHEIN-WESTFALEN (DL) 7.6 9.3 9.8 8.8 2.0 5.0 2.4 1.9 2.5 3.0 3.5 2.6 2.5 0.5 2.4 1.3 2.6 2.8 2.7 3.0 2.0 2.7 2.3 3.5 2.9 3.46 4 6

SAARLAND (DL) 6.5 6.0 8.0 6.7 2.0 3.0 3.0 4.0 2.5 5.0 6.0 3.6 4.2 1.0 2.5 2.1 1.5 1.8 1.7 3.0 2.5 2.8 3.0 3.5 3.3 3.48 5 7

BRUSSELS HOOFDST. GEWEST (B) 8.0 12.0 12.0 10.5 1.0 8.0 1.0 1.5 1.5 2.0 2.0 1.9 3.1 0.5 3.5 1.7 3.0 5.0 4.0 1.0 1.0 1.0 1.5 3.0 2.3 3.52 6 5

EST (F) 3.3 6.8 8.0 5.8 3.0 3.8 1.8 2.8 2.5 5.0 4.0 3.2 6.4 0.8 2.9 2.5 2.8 2.0 2.4 4.0 4.0 4.0 2.3 5.0 3.6 3.56 7 4

RHEINLAND-PFALZ (DL) 6.3 7.7 9.0 7.5 2.7 3.5 3.0 3.3 4.0 4.0 4.0 3.4 2.4 0.5 2.7 1.3 2.0 2.5 2.3 4.3 3.0 3.9 2.8 3.5 3.2 3.60 8 8

BASSIN PARISIEN (F) 3.3 6.5 8.3 5.7 3.9 2.3 3.3 3.5 3.5 4.0 4.5 3.7 7.5 1.0 3.9 3.1 2.8 1.5 2.1 4.0 5.5 4.5 3.0 5.0 4.0 3.93 9 10

ILE DE France (F) 9.5 12.0 10.0 10.6 1.0 11.0 1.0 1.0 1.2 1.0 2.5 2.0 8.4 0.5 4.5 3.1 2.0 5.5 3.8 3.0 1.0 2.3 1.5 4.0 2.8 4.16 10 9

ZUID-NEDERLAND (NL) 9.0 9.5 10.8 9.6 2.8 3.8 2.0 1.5 1.5 3.0 2.8 2.4 2.4 1.0 3.5 1.8 1.8 3.8 2.8 6.0 6.0 6.0 1.5 2.5 2.0 4.16 11 11

OOST-NEDERLAND (NL) 4.7 8.2 11.0 7.6 4.3 3.2 3.3 2.0 2.7 4.0 3.0 3.2 4.9 1.5 3.3 2.6 2.3 2.0 2.2 5.7 6.1 5.8 3.0 2.5 2.8 4.19 12 12

NOORD-NEDERLAND (NL) 2.3 7.3 10.8 6.3 5.0 2.8 4.7 4.0 4.3 5.7 3.7 4.3 7.3 2.7 3.3 3.9 3.3 1.0 2.2 5.0 6.7 5.6 2.8 2.5 2.7 4.57 13 15

WEST-NEDERLAND (NL) 9.3 11.0 11.6 10.5 2.5 6.6 2.0 1.5 1.8 1.9 2.0 2.3 5.9 1.6 3.6 3.0 2.4 4.3 3.3 6.0 4.2 5.4 1.6 2.5 2.1 4.61 14 13

LUXEMBOURG (GR. DUCHE) 12.0 14.0 14.0 13.3 2.5 5.5 2.5 3.0 2.5 3.0 5.0 3.3 4.3 0.5 3.0 1.9 3.0 4.0 3.5 5.0 1.0 3.7 3.0 2.0 2.5 4.87 15 14

mediaan 6.3 8.2 10.8 7.6 2.5 3.8 2.0 2.0 2.5 3.1 3.5 2.6 4.3 0.9 3.4 2.2 2.3 2.5 2.3 4.0 4.0 3.7 2.3 3.4 2.8 3.60

Labour Know-howTotal


33



Attachment F: Explanation of the matrix-elementen.

Domain (weight in matrix)

Matrix-element (weight in domain)

Indicator Sensitivity Source material

Rents (38%)

Rents for logistical warehouses (current norms, 10.000 sq.m.) ; weighted average of the transactions of the last 3 years

+5 €/sq.m./year → +1 point in Ranked Matrix score

C&W research

Land prices (38%)

Prices for industrial land (well equipped and well located); weighted of the transactions of the last 3 years

+30 €/sq.m. → +1 point in Ranked Matrix score

C&W research

Costs (21%)

Labour costs (25%)

Salary costs per employee. Corrections for outliers, strong rural territories, different method of calculation per country, differences between countries in terms of salaries in the transportation sector.

+2600 €/year/employee → +1 point in Ranked Matrix score (this is indicative, and non linear).

Countries: International Labour Organisation and www.ggdc.net Regions: Eurostat Region database + Prices and Earnings, 2009 Edition, UBS

Road density (29%)

Proximity of highway network and 4-line roads

+20 km highway or 4-line roads/1000 sq.km. → -0.2 point in Ranked Matrix score (linear relation, yet exponential relation at the extremities).

Eurostat Region database Michelin roadmaps

Road congestion (7%)

Average length of congestion on the most jammed locations in each region.

+ 30 minutes extra congestion on average/day → +1 point in Ranked Matrix score

Transport & Mobility Leuven for Belgian regions ; Indicatorenboek Duurzaam Goederenvervoer Vlaanderen, 2008; C&W research for all the other regions

Rail density (7%)

Proximity of rail networks (amount of rail line per 1000 sq.km. and per 1000 inhabitants).

Eurostat Region database

Transport System (29%)

Road freight (20%)

Volumes transported + amount of trips from/to the region + average time-distance to transport terminals + accessibility to European markets by road.

Eurostat Region database Connectivity to transport terminals (ICON 2001) “Accessibility index via road” of S&W, published by ESPON

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Rail freight (13%)

Volumes transported + amount of trips from/to the region + average time-distance to transport terminals + accessibility to European markets by rail.

Eurostat Region database Connectivity to transport terminals (ICON 2001) “Accessibility index via road” of S&W, published by ESPON

Airfreight (7%)

Volumes transported + average time-distance to cargo airports + accessibility to European markets by air.

Eurostat Region database Cargo data on airports (C&W) “Accessibility index via road” of S&W, published by ESPON

Shipping freight (20%)

Volumes transported through large seaports (amount of containers when available) + statistics on inland navigation (like IWT)

Eurostat Region database Statistics seaports and inland navigation “Access-time to seaports” (ESPON)

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Domain (weight in matrix)

Matrix-element (weight in domain)

Indicator Sensitivity Source material

Spending power (25%)

Spending power within a 180 minute drive time (in million €)

+ 100 million € → -1 point in Ranked Matrix score

C&W calculation through use of GIS system (CACI), and GfK data.

Access to EU core markets (58%)

Accessibility to the EU-27 countries (+Norway + Swiss), on basis of a gravity model (population and spending power).

+20 points in this index → -0.5 point in Ranked Matrix score

“Accessibility index multimodal/by road” 2001 of S&W, published by ESPON

Accessibility (29%)

Access to Eastern Europe (17%)

Time-distance to important population concentrations in Eastern Europe

+45 min → +0.5 point in Ranked Matrix score

European road network; time-distance calculated with GIS software (Navteq)

New buildings >10000 sq.m. (50%)

Supply in new logistical warehouse locations (> 10,000m²)

1 = immediate availability. 2 = potential availability on the short term, etc., till 5 = no offer.

C&W research Supply (9%)

Land supply (50%)

Supply of land for logistical purposes + available provision

1 = immediate availability. 2 = potential availability on the short term, etc., till 5 = no offer.

C&W research + SPRE (Strat. Plan Ruimtelijke Economie, Vlaand..) + ETIN advisers (www.werklocaties.nl)

Available workforce (67%)

General unemployment figures + unemployment of the younger people (<24)+percentage of younger people (as an indicator of future entry of new workforce on the labour market)

Eurostat Region database Labour (9%)

Labour productivity (33%)

Added-value per employee in the services sector

+1000 € added-value per employee in the services sector → -0.3 point in Ranked Matrix score

Eurostat Region database

Know-How (3%)

Logistics education (50%)

Quantification of the Logistics education and trainings by level and amount of attendances.

+1000 points logistics education → -0.2 point in Ranked Matrix score

FIL intern list of logistics education, local internet lists of trainings.

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Language knowledge (50%)

Knowledge of important European languages. English : Test of English as a Foreign Language (TOEFL). Other languages: estimates per region and country.

+10 on the TOEFL CBT Total Mean score → -1 point in Ranked Matrix score

TOEFL-test data; data on knowledge of French and German per country, corrected with regional presence of migrants.

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Attachment G: Calculation table Buying Power in the 3-hour drivetime perimeter Following table gives for each region the population, the buying power and the ranking score applied in the matrix. The GIS-system that was used for this is CACI.


NUTS code REGION

3 hour drivetime round Population

Total Spending Power ( milj. EUR)

Rank Spending Power

Spending PowerScore

DEA2 KÖLN KÖLN 67,445,618 1,241,549 1 0.2DEA1 DÜSSELDORF DÜSSELDORF 67,148,318 1,233,210 2 0.4NL42 LIMBURG (NL) Venlo 66,685,919 1,218,905 3 0.6DEB1 KOBLENZ KOBLENZ 62,006,170 1,157,073 4 0.8DE71 DARMSTADT (Frankfurt) Frankfurt 58,504,078 1,104,560 5 1.0BE33 LIEGE LIEGE 60,579,596 1,104,147 6 1.2BE34 LUXEMBOURG (B) Neufchateau 60,334,292 1,101,403 7 1.4BE31 BRABANT WALLON Wavre 59,391,812 1,100,764 8 1.6BE22 LIMBURG (B) Genk 60,163,600 1,097,316 9 1.8BE24 VLAAMS BRABANT Vilvoorde 58,464,183 1,079,513 10 2.0BE32 HAINAUT Charleroi 57,155,906 1,069,111 11 2.2DEB2 TRIER TRIER 55,643,932 1,039,368 12 2.4NL22 GELDERLAND Arnhem 56,463,183 1,033,985 13 2.6BE10 BRUSSELS CAP.REGION BRUSSELS 55,527,678 1,032,979 14 2.8DEA5 ARNSBERG ARNSBERG 55,852,544 1,030,959 15 3.0BE23 OOST-VLAANDEREN Gent 54,637,681 1,022,314 16 3.2BE35 NAMUR NAMUR 54,081,941 972,950 17 3.4NL41 NOORD-BRABANT Tilburg 53,558,138 963,806 18 3.6DEB3 RHEINHESSEN-PFALZ Kaiserslautern 50,297,168 959,584 19 3.8LU00 LUXEMBOURG (GRAND DUCHE)LUXEMBOURG 51,156,601 956,560 20 4.0DEC0 SAARLAND Saarbrücken 48,907,543 935,748 21 4.2BE21 ANTWERPEN ANTWERPEN 51,101,230 915,684 22 4.4NL31 UTRECHT REGION UTRECHT 50,158,342 902,925 23 4.6NL21 OVERIJSSEL Zwolle 48,019,330 874,629 24 4.8BE25 WEST-VLAANDEREN Brugge 46,526,800 869,150 25 5.0NL33 ZUID-HOLLAND (Rotterdam) Rotterdam 46,872,025 840,368 26 5.2FR42 ALSACE Strasbourg 41,058,601 835,666 27 5.4NL23 FLEVOLAND Lelystad 45,667,554 828,519 28 5.6FR30 NORD - PAS-DE-CALAIS Lille 44,178,566 819,588 29 5.8NL34 ZEELAND Middelburg 44,973,126 810,100 30 6.0NL32 NOORD-HOLLAND (Amsterdam) Amsterdam 44,137,075 800,160 31 6.2UK73 WEST MIDLANDS (Birmingham) Birmingham 41,367,737 791,780 32 6.4NL13 DRENTHE Assen 41,836,241 770,515 33 6.6NL11 GRONINGEN REGION GRONINGEN 41,331,543 763,479 34 6.8FR21 CHAMP.-ARDENNE Reims 38,674,817 717,913 35 7.0FR41 LORRAINE Nancy 34,447,083 680,672 36 7.2FR22 PICARDIE Amiens 33,638,737 627,041 37 7.4NL12 FRIESLAND Leeuwarden 33,517,133 608,879 38 7.6DEA3 MUNSTER MUNSTER 28,251,879 592,646 39 7.8

UKI1&2 GREATER LONDON GREATER LONDON 29,777,598 586,368 40 8.0FR10 ILE DE FRANCE Paris 31,005,247 576,138 41 8.2IT2 LOMBARDIA (Milano) Milano 28,581,481 570,870 42 8.4

DE21 OBERBAYERN (München) München 28,411,565 568,619 43 8.6DE6 HAMBURG HAMBURG 30,470,406 539,926 44 8.8AT33 TIROL (Innsbruck) Innsbruck 21,562,308 458,054 45 9.0AT32 SALZBURG SALZBURG 20,197,282 392,610 46 9.2DE3 BERLIN BERLIN 24,729,226 382,900 47 9.4FR71 RHONE-ALPES (Lyon) Lyon 17,258,345 320,612 48 9.6IT6 LAZIO (Roma) Roma 16,148,489 246,256 49 9.8

FR82 PROVENCE-ALPES COTE D'AZURMarseille 11,150,818 194,708 50 10.0AT13 WIEN Wien 14,063,705 173,405 51 10.2SK01 BRATISLAVSKY KRAJ BRATISLAVA 13,630,577 148,225 52 10.4CZ01 PRAHA PRAHA 13,471,122 136,482 53 10.6ES3 COM. DE MADRID MADRID 9,249,878 126,238 54 10.8

E04+Cop.DSYDSVERIGE (Malmö)/Öresund Malmö 6,084,276 117,218 55 11.0ES51 CATALUNA (Barcelona) Barcelona 7,549,757 111,316 56 11.2UKM3 SW SCOTLAND (Glasgow) Glasgow 5,499,395 102,658 57 11.4HU01 KOZEP-MAGYAR.(Budapest) Budapest 9,375,108 80,146 58 11.6SE05 VASTSVERIGE (Göteborg) Göteborg 4,113,083 69,964 59 11.8PT13 LISBOA VALE DO TEJO LISBOA 5,733,147 57,885 60 12.0PL07 MAZOWIECKIE (Warszawa) Warszawa 10,459,765 45,886 61 12.2

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By using a ranking method and score one does not get full linearity in the score; following graph gives a view on the linearity of this matrix-element

Buying Power Score vs. Buying Power

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

0 500,000 1,000,000 1,500,000

Spending PowerScore


Attachment H: Thematic maps of the Domains and the total scores, by NUTS-2 region

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Documents

Etude Cushman et Wakefield