model architecture · 2019. 2. 20. · model architecture 3.1. model definition the uman model...

MODEL ARCHITECTURE Deliverable 2.1

5/24/2017

Version 1.1

WP 2

Dissemination level Internal

Deliverable lead Chalmers

Authors Leonardo Rosado (Chalmers University of Technology)

Svetlana Obydenkova (Chalmers University of Technology)

Reviewers Aldo Femia (ISTAT)

Abstract

This report has been prepared within WP2 “Development and implementation of urban metabolism and material flow analysis approach for decision making processes” with the purpose to define the base model to be used for urban material flow analysis (Urban MFA) for eight pilot cities. The report introduces the concept of Economy-wide MFA and its adaptation for the analysis of cities’ metabolism. The suggested model was constructed based on the UMAn model tested on four European cities and described in scientific publications. The report defines the main procedure and boundaries of the analysis of urban metabolism. It provides the scope of statistical data required to feed the model, while more detailed requirements for data collection are set in the corresponding Manuals for data collection. New approach is suggested to predict urban waste based on probability density function. The report summarizes the main indicators, which are expected to be used for analysis of urban metabolism, and their potential interconnection with life-cycle analysis and product environmental footprint, which would allow the UMAn model to enlarge the analysis of cities’ environmental impact as to include indirect flows.

Keywords UMAn model; urban MFA; urban metabolism indicators.

License This work is licensed under a Creative Commons Attribution-No Derivatives 4.0 International License (CC BY-ND 4.0). See: https://creativecommons.org/licenses/by-nd/4.0/

D2.1 report.docx - 1 -

Contents Executive summary .................................................. Error! Bookmark not defined.

1. Introduction ......................................................................................... 2

2. Background .......................................................................................... 2

3. Model architecture ................................................................................. 4

3.1. Model definition ............................................................................... 4

3.2. System boundaries ............................................................................ 5

3.3. Model components ............................................................................ 6

3.3.1. Plugin databases ......................................................................... 7

3.3.2. Calculator............................................................................... 10

3.4. Statistically reported data................................................................. 17

3.4.1. Transport statistics .................................................................... 17

3.4.2. Industrial statistics .................................................................... 17

3.4.3. Agricultural statistics ................................................................. 18

3.4.4. International trade statistics ........................................................ 18

3.4.5. Statistics on waste and air emissions ............................................... 18

3.5. Data quality .................................................................................. 19

4. Model outputs – basis for decision-making process .......................................... 21

5. Links to strategic framework process ......................................................... 25

5.1. Basis for life cycle assessment – the product environmental footprint ............. 25

5.2. Urban profiles and strategic planning framework ...................................... 26

5.3. Basis for project Agoras .................................................................... 26

List of Acronyms ....................................................................................... 27

References .............................................................................................. 29

Appendix – Template “Manuals for Data Collection [Name of the pilot city]” ................ 31


1. Introduction This report is a part of WP2 “Development and implementation of urban metabolism and material flow analysis approach for decision making processes” / T2.1 “Definition of urban flows and related waste impacts” and refers to the project deliverable D2.1 “Model architecture”. It defines the design of the conceptual model and the different components that constitute it, including definition of data requirements.

The main purpose of this report is to define the model that will be used for computation of material flows and urban indicators for eight pilot cities: Bucharest (Romania), Torino, Cremona, Albano Laziale and Pomezia (all in Italy), Manresa and Sabadell (both in Spain), and Leiria (in Portugal).

The report starts with a background about MFA used for analysis of urban areas (Section Two). Section Three introduces the model used for urban MFA, its boundaries and main components. This section provides mathematical interpretation of the model, as well as its blocks, databases, the key requirements for input data. Section Four considers the model outputs and main indicators, which can be used as a basis for the decision making process. Section Five connects the modelling outputs with the other tasks under UrbanWINS umbrella, such as life cycle analysis, strategic planning framework, development of urban profiles and project Agoras.

2. Background The framework of the model used in the project, basically, refers to the methodology defined by Eurostat in Economy-wide material flow accounts (EW-MFA) (Eurostat, 2001). This model takes into consideration physical inputs and outputs into and from an economy, respectively, its accumulation within boundaries and emissions to nature (Figure 2.1).


Figure 2.1. The balance scheme of EW-MFA (Eurostat, 2001)

Set initially to analyze material flows at national level, the Eurostat principles were adopted by researchers (often with methodology adaptation and modification) to accomplish the MFA for urban areas. This resulted in a series of MFA studies for several European cities, such as Paris (Barles, 2009), for NUTS3 regions of Czech Republic (Kovanda et al., 2009), for Lisbon (Niza et al., 2009; Rosado et al., 2014), for Irish city-region (Browne et al., 2011), for Amsterdam (Voskamp et al., 2016), Stockholm, Gothenburg and Malmo (Rosado et al., 2016) and others.

As it can be derived from mentioned studies, while older ones tested the Eurostat methodology for more aggregative accounts of different material flows (Barles, 2009; Kovanda et al., 2009), more recent analysis tend to keep different materials distinct (Rosado et al., 2014, 2016), as well as to consider flows which are not covered by Eurostat method (e.g. of water) (Voskamp et al., 2016). Overall, among the main obstacles to perform a comprehensive urban MFA observed by authors were a lack of information at a local level, as well as a lack of understanding how material flows were interconnected within an urban area. The absence of a standardized procedure appears to be the main barrier for the development of urban MFA.

The current project is based on the Urban Metabolism Analyst (UMAn) model that utilizes the main principles of EW-MFA model, however, it offers some advanced features (e.g. detailed material databases). The UMAn model was developed with the aim to be suitable for the use in urban MFA and to be able to deal with data shortages arising while downscaling the economy from a country level to an urban one (Rosado, 2012; Rosado et al., 2014).

The UMAn model offers a procedure for computation of material flows at urban level, and additionally, it exploits a set of “plug-in” databases allowing more detailed analysis of


those flows. These include a product life cycle phase, material composition of products and lifespan (Rosado, 2012; Rosado et al., 2014). The procedure also allows some advantageous features, e.g. decoupling cross flows from imports and exports (Rosado et al., 2014).

Being supplemented with other methods, such as life cycle assessment (LCA), the UMAn model can become a powerful tool for urban metabolism analysis (Lavers et al., 2017).

The UMAn model was tested in material flow accounting for Lisbon metropolitan area (Rosado et al., 2014), as well as for three metropolitan areas in Sweden: Stockholm, Gothenburg and Malmo (Rosado et al., 2016).

3. Model architecture

3.1. Model definition The UMAn model establishes a methodology for the computation of Material Flow Accounts at urban scale, fit for use in the UrbanWINS project and with the ambition to set a reference standard, or best practice, for this kind of applications. The approach utilized by the model is, basically, derived from EW-MFA model, though the UMAn model adapts this methodology to be used for a smaller spatial unit, such as the urban scale. The model considers different aspects appearing in material flows at an urban level: matter movement through boundaries, extraction and production of materials and goods, their consumption, addition to stock and emissions to nature. Furthermore, the model includes its own plugins allowing more advantageous features compared to models used previously. These benefits described in more details in (Rosado, 2012; Rosado et al., 2014) are as follow:

• The model was supplemented with comprehensive dataset comprising the material

composition of flows

• The model allows obtaining information about the origin and destination of flows

within urban boundaries

• The model allows considering dynamics of addition to stock

• The model allows analysis of dynamics of some waste streams.

The general model scheme is presented in Figure 3.1.


Figure 3.1. The UMAn model block scheme (AR – available resources, ITS – International Trade Statistics)

3.2. System boundaries Two types of system boundaries are established: boundaries between the urban economy and the environment, and between the urban economy under analysis and all other economies that interact physically with it (Figure 3.2).

The first type of boundaries in the UMAn model refers to separating material flows by its origin and “final destination”: natural resources intake from nature, emissions to nature, as well as to waste that does not feature any value for economy and ends up in landfills. Water, air, solar energy and other similar natural resources coming from environment are not treated by the model.

The second type of boundaries refers to material flows that feature value for an urban economy. These flows are accounted in all balances, including changes in stock. These material flows can originate either from the environment, from an urban economy, or from other economies.

In the same manner, by analogy with EW-MFA, changes in stocks of human bodies and livestock are beyond the system boundaries. The latter is mainly due to (i) they can be


considered as negligible compared with man-made material stocks (e.g. buildings and machinery), and (ii) their change over time is considerably small (Eurostat, 2001).

The spatial model boundaries are limited to statistical boundaries defined by Eurostat. Basically, for the urban MFA the model utilizes the NUTS 3 level (NUTS stands for “Nomenclature of territorial units for statistics”). NUTS 3 level refers to small regions for specific diagnoses as it is defined by Eurostat.

However, some cities under analysis refer to smaller than NUTS 3 administrative units. In such cases the model boundaries can be limited to Local Administrative Units (LAU) up to level 1 or level 2 that corresponds to the level of municipalities or equivalent units.

The information about spatial boundaries for eight pilot cities is provided in Table 3.1.

Table 3.1. The model spatial boundaries for eight pilot cities in accordance with the current Eurostat and countries classification

City / Country Statistical boundaries NUTS 3 LAU1 LAU 2

Bucureşti / Romania X - - Albano Laziale / Italy - - X Cremona / Italy X - - Torino / Italy X - - Pomezia / Italy - - X Manresa / Spain - - X Sabadell / Spain - - X Leiria / Portugal - X -

Since administrative boundaries are determined by available data, in some cases, where it is not possible to recover information for NUTS 3 level, the spatial boundaries can be drawn up to NUTS 2 level. Thus, when information is not available at the level of the urban system under analysis – as is the most common situation - the model is fed with the information referred to the smallest available level (territorial unit) comprising it.

3.3. Model components The Urban Metabolism Model comprises several conceptual blocks, the most important of which are plugin databases and calculator. While complemented with statistically reported data as input parameters for accounting material flows at urban level, the model can provide a wide range of outputs as it is shown in Figure 3.1. The detailed definition of the model components is provided below. The description of the model components is given in accordance with Rosado (2012) and Rosado et al. (2014).


3.3.1. Plugin databases Plugin databases added to the model are aimed at describing products lifecycle phase, material composition and products lifespan.

Product life cycle phase

The dataset of product life cycle phase is necessary to separate materials appearing at their different steps of conversion (e.g. raw material, intermediate product, final goods, etc.). However, since some materials can be designated as final products/goods and at the same time can be used as intermediate materials (e.g. some types of milk), the model separates three main life cycle categories: intermediate products (used for manufacture), retail and wholesale (final and intermediate products) and others (final products) (Patrício, 2015). Assignment to one or another life cycle phase is performed based on information about material distribution by economic activity.

Thus, the life cycle phase characterization matrix (!",$) can be defined as:

!",$ = &'(,( ⋯ '(,$⋮ ⋱ '-,. ⋮'",( ⋯ '",$

/ (3.1)

Where i=1,…,n=13135 classes of products; j=1,…,g=3 state (phase) classes of goods.

Material composition

The material composition dataset, designated in the model as ProdChar (stands for products characterization) defines totally 13135 types of products for CN 2007, where 9785 of those products refer to final goods as it is defined by the product life cycle phase. The products considered in the model are defined by the fifth digit of disaggregation of the combined nomenclature (CN8).

The ProdChar dataset was developed based on information about average materials composition supplied by different sources (more details provided in (Rosado, 2012)). For instance, the product type of CN 87032190 (motor cars and other motor vehicles principally designed for the transport of persons) comprises of 4% lubricants and oils and solvents, 22% plastics and rubber, 58% iron, steel alloying metals, and ferrous metals, 2% light metals, 4% nonferrous heavy metals, 1% sand (in the form of glass), 4% textile biomass, and 5% nonspecified (Rosado et al., 2014).

In concordance with their properties, all material flows in the model were split into 28 material categories (Table 3.2).


Table 3.2. Material categories separated in the UMAn model

Material category Aggregated group of materials

Low ash fuels High ash fuels Lubricants and oils and solvents Plastics and rubbers

Fossil fuels

Iron, steel alloying metals, and ferrous metals Light metals Nonferrous heavy metals Special metals Nuclear fuels Precious metals

Metals

Sand Cement Clay Stone Other (fibers, salt, or inorganic parts of animals)

Non-metallic minerals

Agricultural biomass Animal biomass Textile biomass Oils and fats Sugars Wood and fuels Paper and board Nonspecified biomass

Biomass (forestry, crops and animal products)

Alcohols Chemicals and pharmaceuticals Fertilizers and pesticides

Chemicals and fertilizers

Nonspecified Liquids

Others

Thus, all materials defined in ProdChar are presented in the model as the material composition matrix (Mn,m):

0",1 = &2(,( ⋯ 2(,1

⋮ ⋱ 2-,. ⋮2",( ⋯ 2",1

/ (3.2)

Where i=1,,n=13135 classes of products and j=m=28 – types of materials.

The subset of material composition matrix that describes only final goods is defined as:

03,1 = &2(,( ⋯ 2(,1

⋮ ⋱ 2-,. ⋮23,( ⋯ 23,1

/ (3.3)

Where i=1,…,z=9785 classes of products and j=1,…,m=28 – types of materials.


Product lifespan

This database includes information about the average product lifespan (in years) and a distribution function for each product. The dataset of products lifespan was created with the aim to describe material dynamics, thus allowing to obtain information about resources potentially available in the future for reuse, recycling and recovery.

The distribution function chosen is the Weibull probability density function:

4(6, 7, 8) = 4(6) = :;

<×(>

<);?( × @?(

><A )B, 6 ≥ 0

0, 6 < 0 (3.4)

Where x is the duration of a product lifespan, k > 0 is the shape parameter and λ > 0 is the scale parameter of the distribution. For most products, k is equal to 10 that reflects the potential amounts flowing out of the system on an annual basis (Rosado et al., 2014).

By balance method, an economy material throughput is defined as material input, including domestic extraction and imports, minus net addition to stocks and minus exports (Eurostat, 2001). On the other hand, it can be also defined via information about products lifespan, where material throughput implies literally how many products become obsolete each year (Rosado et al., 2014).

The same approach was adopted, inter alia, by Kalmykova et al. (2015) in computing waste electrical and electronic equipment (WEEE) available for recycling in Sweden. The example of the generation of TVs WEEE based on Weibull probability density function is demonstrated in Figure 3.2.

Figure 3.2. TVs WEEE generation (derived from (Kalmykova et al. 2015)); Comment: CRT - cathode ray tube,

LCD - liquid-crystal display. Bars represent the weight of goods consumed in three categories: CRTs, LCDs and Plasma and continuous lines reflect the amount of WEEE expected in the corresponding categories


Thus, the throughput matrix for each product (Tn,y) in the model is defined as:

F",G = HI(,( ⋯ I(,G⋮ ⋱ I-,. ⋮I",( ⋯ I",G

J (3.5)

Where i=1,…,n=13135 classes of products and j=1,…,y years.

3.3.2. Calculator The product and materials accounting is performed in five subsequent steps, where the fifth step includes several parallel operations. The block scheme of the process is shown in Figure 3.3.

The calculation starts from definition of available resources in the system, followed by determination of “Original” matrix. The latter in Figure 3.3 refers to products and goods when they enter the system. The third step (FC / IP) allows splitting between final products, intermediate and raw goods. The “Final” matrix defines products and goods available for the final consumption. In this step, all products, inter alia, can be assigned to specific economic activities via built-in sub-model tool (detailed information is provided in the sections below).

Figure 3.3. Urban Metabolism model - product and materials accounting (FC – final consumption, IP –

industrial production)

Available resources at a spatial unit

Available resources are calculated based on information about imports and exports, coming from/into outside of the country and from/into the other country regions and domestic extraction within analyzed urban area, and crossing flows.


Imports and exports are measured based on transport statistic divided onto four transportation modes: air, railway, water and road transport and also based on energy carriers imported/exported, including electricity.

The mathematical description of the resources available for the urban area is defined as:

KL = KL-1M + KLOP − KLP>M = ∑ ∑ ST-1M + ∑ STOP −UVWMX(

UVWMX(

Y1X( ∑ ∑ ST_@6[UVW

MX(Y1X( (3.6)

Where:

AR – available resources IMP – import

EXP – exports DE – domestic extraction

m – number of transportation modes p – NST category.

However, since transport statistics are reported in NST (“Standard goods classification for transport statistics”), or former NST/R aggregated categories (20 and 24 categories, respectively), while CN for the Trade statistics includes the list of 13135 of commodity types, these two classifications need to be harmonized by means of correspondence of NST to CN.

For international flows, this extrapolation is performed based on international trade statistics and for national flows this is made based on data of domestic extraction, industrial production, as well as imports and exports from other countries. It is also necessary to account for the spatial data distribution (Figure 3.4).

Figure 3.4. Scheme of material flows in three spatial levels: international, national and urban

“Original” matrix

At the next calculating stage, the total amount of each NST category is split by specific CN category in a percentage related to the sum of all CN product types that belong to the mentioned NST category.


The import of international goods/products by CN code is calculated based on information about imported goods/products defined in NST category that is multiplied by the ratio of this goods/products by CN code to the sum of international goods/products by CN code belonging to the corresponding NST category:

44(T@\]^_)(Ù) =-a(bP$-c")(de)

∑ -a(bP$-c")(de∈egh)× 44(1,UVW) (3.6)

International trade with urban region as destination (it(region)(CN)) by CN code is defined via a number of employees (w) involved in the corresponding economic activity:

]I(T@\]^_)(Ù) = ]I(i^j_ITk)(Ù) ×l(bP$-c")emdnl(opqrstu)emdn

(3.7)

National import of goods/products by CN code is accounted based on amount of imported goods/products by NST category and a ratio of different goods/products by CN code in each NST category:

_4(T@\]^_)(Ù) =-a(bPva)(de)w"M(bPva)(de)wOP(bPva)(de)

∑(-a(bPva)(de∈egh)w"M(bPva)(de∈egh)wOP(bPva)(de∈egh))× _4(T@\]^_)(1,UVW)

(3.8)

Where:

ff – foreign flows nf – national flows

it – international trade np – national production

de – domestic extraction region - urban region as destination

rest – import from rest of the country

m – number of transportation modes (1…4) CN=1,…,13135 – CN code

NST – NST category (1,…,20 for NST 2007 or 1,…,24 for NST/R).

Thus, the “original” matrix of available resources (AR) in an urban area can be defined as:

KLx = HST-1M( + STOP( − STP>M(

………ST-1Mz + STOPz − ST{|}z

J (3.9)


Where f=1,…,13135 – CN category.

AR is a valuable parameter reflecting material consumption of a spatial unit. Thus, the UMAn model allows to obtain an information on consumption patterns for up to n classes of CN.

FC/IP

In the model, it is assumed that final goods available in a spatial unit go directly to consumption, while all other goods are first transformed into final goods in order to be consumed in that area (Figure 3.5).

Figure 3.5. Destination of available resources in a spatial unit

The mathematical description of separation of final goods from goods that are going to be transformed is done by means of multiplication of the available resources vector transformed into diagonal matrix and the matrix defining the product phase:

(3.10)

Where n=1,…,13135 classes of products; g=1…3 state (phase) classes of goods.


“Final” matrix

The matrixes for transformation and for final consumption in urban area are defined as:

~ab�"vxr = &^(,( + ^(,Ä………

^",( + ^",Ä/ (3.11)

~Åc"vr = &^(,Ç…^",Ç

/ (3.12)

Where n=1…13135 classes of products; g=1…3 state (phase) classes of goods.

Thus, domestic material consumption (DMC) can be written as:

É0Ñ" = ~Åc"vr + ~ab�vxr (3.13)

Where n=1,…,13135 classes of products.

The transformation of pre-processed goods into final ones that include destination by economic activity is being made by extrapolation of information from industrial production statistics (Figure 3.6). Along with goods, this step takes into consideration all emissions to nature appearing during production activity.

Figure 3.6. Transformation sector

Products to materials

The material consumption (MC) of a spatial unit is the key feature of the model. This can be defined via the next equation:


0Ñ",1 = É0Ñ"," ×Ö 0",1

= HÜ2i(,( ⋯ 0⋮ ⋱ ⋮0 ⋯ Ü2i","

J × &2(,( ⋯ 2(,1

⋮ ⋱ ⋮2",( ⋯ 2",1

/ (3.14)

Where n=1,…,13135 classes of products and m=1,…,28 – types of materials.

The material consumption detailed to up to n classes of CN, can be used as indicator of consumption preferences of a spatial unit. When it is combined with the analysis of environmental footprints, it can provide invaluable input for strategic planning with the overall aim of shifting towards those consumption pattern that feature lower environmental impact.

Material throughput

Material throughput is calculated by multiplying the diagonal matrix of domestic material consumption by the matrix comprising lifespan database:

áF",G = É0Ñ"," ×Ö F",G

= HÜ2i(,( ⋯ 0⋮ ⋱ ⋮0 ⋯ Ü2i","

J × HI(,( ⋯ I(,G⋮ ⋱ ⋮I",( ⋯ I",G

J (3.15)

Where n=1,…,13135 classes of products and y=1,…x years.

Material throughput obtained via this equation allow to obtain information about waste expected in a spatial unit and its composition. This might become a basis for the planning properly waste prevention and management systems.

Distribution by economic activity

At this stage, all products can be assigned to specific economic activities. The latter is performed based on information derived from international trade statistics (ITS) and with an assumption that domestic products feature the same destination as imports of goods.

The activity distribution (AD) matrix given in percentage is defined as:

KÉ3,Å = HSÜ(,( ⋯ SÜ(,Å⋮ ⋱ SÜ-,. ⋮

SÜ3,( ⋯ SÜ3,ÅJ (3.16)


Where i=1,…,z=9785 classes of products and j=1,…,c=99 two-digit NACE code1.

Thus, finally the distribution of materials per economic activity (EA) can be found by multiplying the diagonal matrix of domestic material consumption by the matrix of activity distribution:

àK3,Å = É0Ñ3,3 ×Ö KÉ3,Å

= HÜ2i(,( ⋯ 0⋮ ⋱ ⋮0 ⋯ Ü2i3,3

J × HSÜ(,( ⋯ SÜ(,Å⋮ ⋱ ⋮

SÜ3,( ⋯ SÜ3,ÅJ (3.17)

Spatial distribution

The spatial distribution in the model is described as matrix that represents a mix of economic activities appearing in different locations. To be able to create such matrix, there was an assumption made about linear relation between number of workers occupied in the specific economic activity and the capacity of the corresponding activity:

'ÜÅ,1â" =lo∑lo

(3.18)

Where mun=1,2,..,n municipalities and c=1,…,99 two-digit NACE code1.

The spatial distribution (SD) of economic activities matrix can be designated as:

!ÉÅ,1â" = H'Ü(,( ⋯ 'Ü(,1â"⋮ ⋱ 'Ü-,. ⋮

'ÜÅ,( ⋯ 'ÜÅ,1â"J (3.19)

Where i=1,…,c=99 two-digit NACE code1, j=1,..,mun municipalities.

Thus, finally the vector of distribution of materials per economic activity and per defined previously special unit (NUTS 3, LAU1 or LAU2) designated as MSD vector can be found by multiplying the economic activity distribution matrix by the vector of distribution of economic activities in municipality:

0!É3,( = àK3,Å × !ÉÅ,(

1 This example utilizes two-digit NACE code, though it could be extended in the case when data for deeper level of NACE disaggregation is available.


= &@S(,( ⋯ @S(,Å⋮ ⋱ ⋮

@S3,( ⋯ @S3,Å/ × &

'Ü(,1â"(…

'ÜÅ,1â"(/ (3.20)

Where z=1,…,9875 classes of products, c=1,…,99 two-digit NACE code2.

3.4. Statistically reported data As a standardized tool, the model utilizes statistically reported data supplied by Eurostat and national statistical offices. The range of statistics required to feed the model was defined within Task 2.2 of the project and spread out via Manuals for Data Collection for each of eight pilot cities (template of Manual for Data Collection is provided in Appendix). Basically, this statistical information includes transport statistics, industrial statistics, agricultural statistics, international trade statistics, statistics on waste and emissions.

3.4.1. Transport statistics Transport statistics fed to the model are supplied in NST statistical nomenclature, that stands for “Standard goods classification for transport statistics”. This nomenclature covers goods transported by four transport modes, namely road, rail, inland waterways and sea (maritime). Currently used NST 2007 classification comprises 20 divisions of goods/materials that are further disaggregated into groups.

3.4.2. Industrial statistics Industrial statistics used in the model can be reported either by NACE or in PRODCOM statistics, or in CN.

NACE (from French “Nomenclature générale des Activités économiques dans les Communautés Européennes”) stands for “Statistical classification of economic activities in the European Communities”. Thus, in NACE statistical data is presented by economic activity in the fields of economic statistics (e.g. production, employment, national accounts). Overall, industrial statistics are covered by five sections of NACE Rev.2, namely: extractive industries (sections B), manufacturing industry (section C), production and distribution of electricity, gas and steam (sections D), water supply, sewerage, waste management and remediation activities (section E) and construction industry (section F) (NACE Rev.2, 2008). The NACE hierarchical structure comprises four levels, which

2 This example utilizes two-digit NACE code, though it could be extended in the case when data for deeper level of NACE disaggregation is available.


designate, first, sections, then headings identified by a two-digit (divisions), three-digit (groups) and four-digit (classes) numerical codes.

PRODCOM supplies production statistics for mining and manufacturing (i.e. excluding services, other than “industrial services”). Prodcom uses the product codes specified on the Prodcom List, which contains about 3900 different types of manufactured products. Products are identified by an 8-digit code. The remaining digits specify the product in more detail.

3.4.3. Agricultural statistics

Current agricultural statistics is covered by Council Regulation 543/2009 that repeals Council Regulation 837/90 and Council Regulation 959/93 (Eurostat, 2015). The agricultural statistics report data on various crop products or groups of products linked to cultivated, harvested and production areas, production, yields and agricultural land use.

3.4.4. International trade statistics International trade statistics (ITS) provide information on goods (i.e. all movable property including electricity) traded between a EU Member State and a non-EU-member country, as well as between the EU Member States. The ITS data is reported in Combined Nomenclature (CN) with eight-level of disaggregation.

The UMAn model generally uses the Combined nomenclature (CN) for goods classification. CN was established by the Council Regulation (EEC) No.2658/87 in order to meet both the Common Customs Tariff and of the external trade statistics of the European Community (EC).

The CN is revised on a yearly basis, resulting in a set of separate CN revisions released for years from 1995 to 2017. Since UMAn model utilizes an arbitrary CN version, all other revisions are transformed by the model into the arbitrary nomenclature.

3.4.5. Statistics on waste and air emissions

The framework and requirements to waste and emission statistics are established by the European regulations: by the Regulation (EC) No 2150/2002, Regulation (EU) No 691/2011 and its Annex 1.

Regulation on waste statistics set out to collect and transmit statistics on the generation, recovery and disposal of waste. In accordance with this document, the waste statistics shall be provided for all sectors of economic activity defined by NACE, but it covers also waste generation by households and waste produced by recovery and disposal operations (Regulation (EC) No 2150/2002, Annex 1).


The overall requirements to air emission statistics compilation are covered by Regulation (EU) No 691/201 and, in particular, the Annex 1 to this document provides specific characteristics to air emission accounts, where the characteristics are linked to classification of economic activities defined by NACE.

3.5. Data quality The reliability of the modelling results, first, is ensured by the quality of input data. This especially becomes an issue when data found for bigger statistical regions are scaled down to provide estimates for smaller areas. Eventually, the quality of the data will affect MFA indicators.

For the data reported by Eurostat, the latter sets its own precision requirements. Some of the requirements are also established by the EU laws that induce Member States to meet a certain level of statistical quality in the collection and transmission of statistical results (EC, No 223/2009). For instance, the requirements to the carriage of goods by road declares that, basically, for statistics performed based on sample methodology, the standard error of the annual estimates should not exceed ±5% within the confidence interval of 95% and only for specified number of vehicles it is allowed to consider standard error of ±7% for the same confidence interval (EC, 2004). Data supplied to Eurostat by national statistical offices are accompanied with quality reports.

In a broader MFA context, however, some authors believe that for the confidence interval of 95% the standard error of ±25% can be still considered as bearable (Rechberger et al., 2014). The latter might become a measure of results reliability for urban MFAs. At the same time, given uncertainty estimates provided for inputs by Eurostat, as well as by other statistical bodies and special institutions, the data reliability can be assessed qualitatively (Patrício et al., 2015). Some of those estimates are showed in Table 3.3.

Table 3.3. Estimates on input data quality based on statistics reported by Eurostat

Input data Data quality for spatial unit Comment Country NUTS 2 NUTS 3 LAU1/

LAU2 Road transport

• National • International

A A

A -

A -

- -

Statistics reported by NST category

Rail transport • National • International

A A

A -

- -

- -

Statistics reported by NST category

Air transport • National • International

A A

A A

A A

A A

Without classification per NST category

Water transport A A A A Without classification per NST category

Oil pipeline transport A - - - - Waste by economic activity

A C C C Most of the data are reported by aggregated groups of economic


Input data Data quality for spatial unit Comment Country NUTS 2 NUTS 3 LAU1/

LAU2 activity

Air emissions, including GHG emissions

A C C C Emissions reported by economic activity aggregates. GHG emissions reported in CO2-equivalent

Agricultural production: • Crops • Milk • Meat

A A A

A A C

C C C

C C C

For milk production for NUTS 2: data is available for cows’ milk

Fishery production A C C C - Forestry production A C C C - Minerals and fossil fuels extraction

A C C C Most of the data available via Eurostat refer to confidential information

Energy services production

A C C C -

Industrial production by CN or by PRODCOM code

A C C C Some of the data available via Eurostat can refer to confidential information

International trade in goods by CN8

A - - - Data available via Eurostat refer to confidential information

Population A A A - - Number of persons employed by NACE category

A A - - -

Legend to the Table: A – data reported by Eurostat – methodology of data collection is well described; C – data can be computed by the model.

As it follows from the table, most of the data that refer to smaller spatial units are not available via Eurostat. To provide higher data reliability, such data, first, shall be requested from national, regional or local statistical offices and if it is not available, some of the data can be computed, though it is clear that precision accuracy will be affected by such types of operations.


4. Model outputs – basis for decision-making process

The main indicators calculated by the model are the same as those ones used in EW-MFA, though the model also offers some advanced features. The model allows calculation of the next main indicators3:

• Direct material input (DMI) - measures the direct input of materials for use into the

economy, i.e. all materials which feature economic value and are used in

production and consumption activities. DMI is calculated as follow:

É0ä = Éà + ä2[^TI' (4.1)

Where DE is domestic extraction.

• Domestic material consumption (DMC) – measures the total amount of material

directly used in an economy (i.e. excluding indirect flows):

É0Ñ = É0ä − à6[^TI' (4.2)

• Domestic processed output (DPO) - measures the total weight of materials which

are released back to the environment after having been used in the domestic

economy. These flows occur at the processing, manufacturing, use, and final

disposal stages of the production-consumption chain (i.e. emissions to air,

landfilled wastes deposited in controlled and uncontrolled landfills, material loads

in wastewater and dissipative flows).

• Net Additions to Stock (NAS) – provides information about the quantity (weight) of

new construction materials used in buildings and other infrastructure, and materials

incorporated into new durable goods such as cars, industrial machinery, and

household appliances:

ãK! = É0Ñ −Éáå (4.3)

• Physical trade balance (PTB) – defined as a difference between physical imports

and physical exports. Thus, a physical trade surplus indicates a net import of

materials, whereas a physical trade deficit indicates a net export:

áFç = ä2[^TI' − à6[^TI' (4.4)

All these indicators can be presented in both absolute values or in per capita basis.

3 Definition is provided in accordance with (Eurostat, 2001)


Other indicators, like domestic extraction, industrial production, imports and exports, crossing flows can be also applied to evaluate urban metabolism in more details. In particular, crossing flows as indicator can be quite useful for the representation of urban metabolism of those cities, which economic activities are highly dependent on logistic operations (e.g. cities with large ports). Crossing flows represent amount of those goods and materials, which materially cross spatial city boundaries, but do not take a part in the city economy.

In addition to the basic indicators, it was suggested to use eight characteristics that allow more insights into urban metabolism (UM). Those characteristics were suggested by (Rosado et al., 2016) and were tested on three cities in Sweden. They are as follow:

• Material needs characteristic (MNC): the characteristic helps understanding the

role of material flows in an urban economy. This indicator utilizes DMI and DMC to

calculate the proportion of the total materials needs that goes to final

consumption.

• Accumulation of materials characteristics: used to compute amount of materials

stored in an urban area, thus providing more insights in amounts of materials

potentially available for reuse or recycling at the end of product lifetime. This

characteristic is calculated based on NAS indicator with use of the own model

plugin.

• UM efficiency characteristics: this characteristics accounts for a share of waste

recycling in the DMC. This characteristic in connected intrinsically with MNC as with

the growth of UM efficiency the MNC decreases. However, the additional qualitative

assessment shall be conducted within this group. This is stemming from

understanding that recycling and reuse are preferable option compared to recovery

and, hence, should dominate over that in a sustainable economy.

• Existence of diverse processes (DP) in urban areas: By analogy to ecosystems, urban

areas with more DP are expected to sustain easier changes and, hence, reveal a

higher level of resilience. The assessment of DP can be made based on analysis of

the proportion of different material types used in DE and IP.

• Support provided by an urban area to other areas: the indicator aims to display the

role of the city in terms of other system and their needs. This assessment is based

on data about export of goods and materials.

• Dependency on other systems: this indicator reveals to which extent an urban area

is dependent on other outer areas, either regional or global. This characteristic

uses DMI and data on international and intra-national import.

• Self-sufficiency of an urban area: this characteristic is quantified by comparing

local resources with the amount of consumption, using the DE, NAS, and DMC

indicators, thus providing an understanding about a “true” amount of resources

available (not equal to AR).


• Pressure on the environment: this characteristic can be divided into two

components: outputs to nature and contribution to depletion of resources, which

are processed with use of DPO and non-renewable and renewable DMI indicators.

Figures 4.1 – 4.3 demonstrate example of representation of several indicators (domestic extraction and industrial production, imports and exports, domestic material consumption and net addition to stock as it was showed in (Rosado et al., 2016). Although the indicators are demonstrated in highly aggregated way, they allow observing differences in urban metabolism between cities and analyzing prerequisites for that. In fact, the indicators can be calculated for all 28 material categories.

Figure 4.1. Domestic extraction and industrial production per capita for the three metropolitan areas in

Sweden (derived from (Rosado et al., 2016))


Figure 4.2. Imports and exports per capita for the three metropolitan areas in Sweden (derived from (Rosado

et al., 2016))

Figure 4.3. Domestic material consumption and net addition to stock per capita for the three metropolitan

areas in Sweden (derived from (Rosado et al., 2016))


5. Links to strategic framework process

The Urban MFA that is based on applying the UMAn model is one of the most important foundations of the whole strategic framework process. Numerical results from MFA, indicators discussed in Section Four and main highlights found through the modelling are supposed to be utilized in other later project tasks and WPs, as well as they are supposed to be used as discussion activators in project Agoras.

5.1. Basis for life cycle assessment – the product environmental footprint

The Roadmap to a Resource Efficient Europe is aimed to push economies for delivery greater value to a society with less environmental impact, via more efficient resource use and minimizing their impact on the environment (EC, 2011). To this end, the understanding of consumption patterns and its connection to life-cycle impacts becomes vital.

Although a life cycle assessment (LCA) is out of the scope of the UMAn model and the current report thereof, given appropriate output parameters from the model, LCA can successfully complement it.

As it was discussed in Section Three, goods or products can be consumed within urban boundaries while appearing at different lifecycle phases (e.g. as raw materials, intermediate or final products, etc.). Before being consumed, the products can be either imported or domestically produced. However, regardless that, the product life cycle still can carry global environmental impact. The environmental impact can also become a key issue if that one features pronounced local environmental effect.

The model serves as a basis for subsequent assessment of environmental footprints of consumption patterns in cities with the aim to display products causing determinative impact, which, hence, should be addressed first in local, regional or national policies and strategies.


5.2. Urban profiles and strategic planning framework

The success of an urban policy depends on how it responds to real city needs and identified hotspots. From this point of view, indicators computed via UMAn model for eight pilot cities become valuable metrics for compiling the basis for further strategic planning.

The system of indicators determined in Section Four allows advanced analysis of urban profiles with respect to their material flows. As a result, different types of urban metabolism shall be revealed that, ultimately, might help to comprehend the proximity of an urban economy to the principles of circularity (EASAC, 2016) and its movement towards green economy (ESPON, 2013).

This analysis shall be provided in details in D.2.3 “Urban Metabolism case studies: Reports for each of the 8 cities that will be subject to detailed study with quantification and analysis of their Urban Metabolism”. The outputs of this deliverable will be used in the development of local strategic action plans as it was defined in WP 4.1.

5.3. Basis for project Agoras The representation of the modelling results to be used in the project Agoras will be defined by the tasks assigned to those Agoras. Basically, the results should enlight the main production and consumption patterns, and wastes associated with them. Thus, the compiled picture of urban metabolism shall contribute to recognizing patterns that most likely influence waste prevention and management systems. Time series analysis will help to identify main trends and tendencies in such patterns and connect them with waste prevention and management strategies. More detailed information from the modelling can be provided upon stakeholder request.


List of Acronyms AD – Activity distribution

AR – Available resources

CN – Combined nomenclature

DE – Domestic extraction

DMC – Direct material consumption

DMI – Direct material input

DP - Diverse processes

DPO – Domestic processed output

EW-MFA - Economy-wide material flow accounts

EXP – Export

FC – Final consumption

FF – Foreign flows

GHG – Greenhouse gases

IPS – Industrial Production Statistics

ITS – International Trade Statistics

LAU - Local Administrative Units

MC – Material consumption

MFA – Material Flow Accounting

MNC - Material needs characteristic

NAS – Net addition to stock

NF – National flows

NP – National production

NST - Standard goods classification for transport statistics

NUTS - Nomenclature of territorial units for statistics

PEF - Product environmental footprint

PTB - Physical trade balance

UM – Urban metabolism

UMAn - Urban Metabolism Analyst


WEEE – Waste electrical and electronic equipment

WP – Work Package


References Barles, S. (2009). Urban metabolism of Paris and its region. Journal of Industrial Ecology,

13(6), 898–913. https://doi.org/10.1111/j.1530-9290.2009.00169.x

Browne, D., O’Regan, B., & Moles, R. (2011). Material flow accounting in an Irish city-region 1992-2002. Journal of Cleaner Production, 19(9–10), 967–976. https://doi.org/10.1016/j.jclepro.2011.01.007

EASAC. (2016). Indicators for a circular economy.

EC. (2004) COMMISSION REGULATION No 642/2004 of 6 April 2004 on precision requirements for data collected in accordance with Council Regulation (EC) No 1172/ 98 on statistical returns in respect of the carriage of goods by road.

EC. (2011) Communication from the Europepean Parliament, The Council, The European Economic and Social Committee of the Regions - Roadmap to a Resource Efficient Europe

ESPON. (2013). Territorial Potentials for a Greener Economy (GREECO). Interim Report.

THE EUROPEAN PARLIAMENT AND THE COUNCIL OF THE EUROPEAN UNION. Regulation (EC) No 2150/2002 of the European Parliament and of the Council of 25 November 2002 on Waste Statistics

THE EUROPEAN PARLIAMENT AND THE COUNCIL OF THE EUROPEAN UNION. REGULATION (EC) No 223/2009 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 11 March 2009

THE EUROPEAN PARLIAMENT AND THE COUNCIL OF THE EUROPEAN UNION. Regulation (EU) No 691/2011 of the European Parliament and of the Council of 6 July 2011 on European Environmental Economic Accounts

Eurostat. (2001). Economy-wide material flow accounts and derived indicators. https://doi.org/ISBN 92-894-0459-0

Eurostat. (2015). Eurostat Handbook for Annual Crop Statistics ( Regulation 543/2009 Gentlemen’s/ESS agreements)

Kalmykova, Y., Patricio, J., Rosado, L., & EO, B. P. (2015). Out with the old, out with the new - The effect of transitions in TVs and monitors technology on consumption and WEEE generation in Sweden 1996-2014. Waste Management, 46, 511–522. https://doi.org/10.1016/j.wasman.2015.08.034

Kovanda, J., Weinzettel, J., & Hak, T. (2009). Analysis of regional material flows: The case of the Czech Republic. Resources, Conservation and Recycling, 53(5), 243–254. https://doi.org/10.1016/j.resconrec.2008.12.004


Lavers, A., Kalmykova, Y., Rosado, L., Oliveira, F. and Laurenti, R. (2017). Method for quantification of consumption driven environmental impact at the product level: selection of representative products for a region (forthcoming).

NACE Rev.2 (2008) Statistical classification of economic activities in the European Community

Niza, S., Rosado, L., & Ferrdo, P. (2009). Urban metabolism methodological advances in urban material flow accounting based on the lisbon case study. Journal of Industrial Ecology, 13(3), 384–405. https://doi.org/10.1111/j.1530-9290.2009.00130.x

Patrício, J. (2015). Applying the UMAn Model Tutorial MFA at urban level. Draft Version xx.xx.2015. Chalmers University of Technology

Patrício, J., Kalmykova, Y., Rosado, L., & Lisovskaja, V. (2015). Uncertainty in material flow analysis indicators at different spatial levels. Journal of Industrial Ecology, 19(5), 837–852. https://doi.org/10.1111/jiec.12336

Rechberger, H., Cencic, O., & Fruhwirth, R. (2014). Uncertainty in material flow analysis. Journal of Industrial Ecology, 18(2), 159–160. https://doi.org/10.1111/jiec.12087

Rosado, L. (2012). A standard model for urban metabolism. Instituto Superior Tecnico.

Rosado, L., Niza, S., & Ferrão, P. (2014). A Material Flow Accounting Case Study of the Lisbon Metropolitan Area using the Urban Metabolism Analyst Model. Journal of Industrial Ecology, 18(1), 84–101. https://doi.org/10.1111/jiec.12083

Rosado, L., Kalmykova, Y., & Patrício, J. (2016). Urban metabolism profiles. An empirical analysis of the material flow characteristics of three metropolitan areas in Sweden. Journal of Cleaner Production, 126, 1–12. https://doi.org/10.1016/j.jclepro.2016.02.139

Voskamp, I. M., Stremke, S., Spiller, M., Perrotti, D., van der Hoek, J. P., & Rijnaarts, H. H. M. (2016). Enhanced Performance of the Eurostat Method for Comprehensive Assessment of Urban Metabolism: A Material Flow Analysis of Amsterdam. Journal of Industrial Ecology, 0(0), 1–16. https://doi.org/10.1111/jiec.12461


Appendix – Template “Manuals for Data Collection [Name of the pilot city]”

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 690047.

URBANWINS Urban metabolism accounts for building waste management innovative

networks and strategies

Grant Agreement No. 690047

Template “Manual for Data Collection - [Name of the pilot city]”

WP2 – Development and implementation of urban metabolism and MFA approach for decision

making processes

T2.1 Definition of urban flows and related waste impacts

Document - version X

DD/MM/YYYY

Participants: Chalmers (Leader); CTM, ISTAT, Ecotic, SERI, Consorci del Bages, Linea Gestioni, Nova.id.FCT, RoGBC, CEIFACOOP

2

Contents Consent form from Ethics requirements ............................................................................................ 5

Description of the task in the project proposal................................................................................... 6

Main terms and classifications ........................................................................................................... 7

Spatial level - NUTS Level ............................................................................................................... 7

Product level - NST Category .......................................................................................................... 7

Economic activities - NACE Category .............................................................................................. 9

Product level and economic activities - Statistical classification of products by activity (CPA) ....... 12

Product level - Harmonized system (HS) ....................................................................................... 13

Product level - The Combined Nomenclature (CN) ....................................................................... 14

Product level - PRODCOM ............................................................................................................ 16

Agricultural statistics ................................................................................................................... 17

Inventory of data sources to collect ............................................................................................. 21

1. Road Transport ........................................................................................................................ 21

1.1. International Road Transport ........................................................................................... 21

1.1.1. Loaded and unloaded goods by <NUTS 3> per NST category ..................................... 21

1.2. National Road Transport .................................................................................................. 22

1.2.1. Loaded and unloaded goods by <NUTS 3> per NST category ..................................... 22

2. Rail Transport .......................................................................................................................... 22

2.1. International Rail Transport .............................................................................................. 22

2.1.1. Amount of goods loaded and unloaded by <NUTS 3> per NST category .................... 22

2.2. National Rail Transport ..................................................................................................... 23

2.2.1. Amount of goods loaded and unloaded in <NUTS 3> per NST category ..................... 23

3. Water Transport ...................................................................................................................... 24

3.1. International Water Transport .......................................................................................... 24

3.1.1. International goods loaded and unloaded by Ports in <NUTS 3> per NST category .... 24

3.2. National Water Transport ................................................................................................. 25

3.2.1. National goods loaded and unloaded by Ports in <NUTS 3> per NST category ........... 25

4. Air Transport ............................................................................................................................ 26

4.1. International Air Transport ............................................................................................... 26

4.1.1. International goods loaded and unloaded and mail by airports in <NUTS 3> per NST category 26

4.2. National Air Transport ...................................................................................................... 27

4.2.1. National goods loaded and unloaded and mail by airports in <NUTS 3> per NST category 27

5. Oil and gas pipeline transport .................................................................................................. 28

3

5.1. International transport loaded and unloaded in <NUTS 3> ............................................... 28

5.2. National transport loaded and unloaded in <NUTS 3> ...................................................... 28

5.3. Transit by <NUTS 3> ......................................................................................................... 28

6. Waste by Economic Activity ..................................................................................................... 28

6.1. Data available for <NUTS 2> ............................................................................................. 28


7. Municipal waste, WEEE, portable batteries and accumulators, end-of-life vehicles .................. 29



8. Air emissions – CO2 emissions by origin (biomass and fossil fuels) ........................................... 32



9. Sludge (Solid Part) from Waste Water Treatment ..................................................................... 33



10. Primary Crops, Fodder Crops including grassland harvest, Fruits, Vegetables, Berries, Nuts, Flowers, etc. agricultural production ............................................................................................... 33

10.1. Data available for <NUTS 0> ......................................................................................... 33


11. Wood: Harvested timber wood and fuel wood ..................................................................... 34



12. Sea and inland fish catch, crustaceans, mollusks and aquatic invertebrates .......................... 34



13. Milk and milk products ......................................................................................................... 35



14. Primary meat production (slaughtering) ............................................................................... 35



15. Primary eggs production ...................................................................................................... 35



16. Minerals Extraction .............................................................................................................. 36

16.1. Extraction of metallic minerals (copper, lead, zinc, iron, gold, silver, etc.) ..................... 36

4

16.1.1. Data available for <NUTS 0>...................................................................................... 36


16.2. Extraction of non-metallic minerals (limestone, quartz, clay, feldspar etc.) ................... 37



17. Sand and Gravel Extraction .................................................................................................. 38



18. Fossil and Nuclear Fuels Extraction ....................................................................................... 40



19. Electricity, Gas, Steam, Air conditioning Production and Consumption ................................. 41



20. Industrial Production by CN or by PRODCOM code ............................................................... 42


21. International Imports and Exports of Goods by CN code....................................................... 42


22. Population ........................................................................................................................... 46



22.3. Data available for municipality level (LAUs Level 2) ....................................................... 46

23. Number of employees per NACE category ............................................................................ 46



23.3. Data available for municipality level (LAUs Level 2) ....................................................... 47

References ................................................................................................................................... 48

5

Consent form from Ethics requirements

[to be added when available]

6

Description of the task in the project proposal

[for reference only]

WP2 – Development and implementation of urban metabolism and MFA approach for decision making processes

T2.1 Definition of urban flows and related waste impacts

In this task, the base model will be built and run for the 8 cities involved in the project. The base model at this stage will be kept open to changes that might reflect the specific needs of the cities for waste management decision-making. The definition of the model will include:

1) The definition of system boundaries: in particular spatial boundaries, statistical boundaries, types of resources quantified; types of environmental impacts accounted, waste prevention and management strategies; sectorial distribution of products and waste, including household consumption.

2) Identification of the maximum level of disaggregation that will allow relevant information for waste management decision-making. The main variables that will be explored include: level of disaggregation of products (for example, 1000 product types that cover all products in the urban system), types of environmental impacts (e.g, GHG emissions, eutrophication, ozone depletion, among others), types of materials (considering potential for recycling, reuse and valorization), spatial levels (example, allocation of flows to the neighbourhood level); estimation of obsolescence of products through time (example, by using lifetime of products an estimate of the stock of cities can be made and a projection of future wastes production is possible); allocation to economic sectors inside the city (example, services, health sector, education, household sector, public sector) – this assumes a particular relevance because it allows a flexible use of the results for each specific case based on the influence stakeholders might have in the decision-making process in different cities. Furthermore, the household sector will also be of particular relevance, since consumption is a major driver of material flows in urban areas, and therefore an investigation on the socio-economic indicators of families need to be addressed, such as household size, income, education, gender distribution, age types, among others.

7

Main terms and classifications Spatial level - NUTS Level The NUTS classification (Nomenclature of territorial units for statistics) is a hierarchical system for dividing up the economic territory of the EU. The NUTS classification has been determined to up to 3 levels:

• NUTS 1: major socio-economic regions • NUTS 2: basic regions for the application of regional policies • NUTS 3: small regions for specific diagnoses

While NUTS 0 defines a country level.

For cities featuring smaller than NUTS 3 spatial level: However, the cities considered in this manual refer to relatively smaller administrative units than NUTS 3 and are defined by the system of Local Administrative Units (LAUs), where LAU level 2 (formerly NUTS level 5) represents municipalities or equivalent units. In this case NUTS 3 (code XXXXX) refers to [Name of the metropolitan area] that consist of [number] LAU 2 units, including [name of the city].

Since the European statistics are usually provided with detalization to up to NUTS 3 level, the UMan model is based on NUTS 3 level as the smallest administrative unit for material flow accounting, though some useful extrapolations can be still provided for lower administrative units. To this end, the majority of input information within this manual is being asked for NUTS 3 level and only some types of specific information shall be specified for LAU 2 level.

The information about NUTS classification for [name of NUTS 3 region] is provided in Table 1.

Table 1. [name of NUTS 3 region] - NUTS classification

NUTS Level Versions: YYYY Code Description

0 XX [name the country] 1 XXX [name of NUTS 1 region] 2 XXXX [name of NUTS 2 region] 3 XXXXX [name of NUTS 3 region]

Product level - NST Category According to Eurostat1, abbreviation NST stands for “Standard goods classification for transport statistics”; it is a statistical nomenclature for the goods transported by four transport modes, namely: road, rail, inland waterways and sea (maritime).

NST (2007) classification, comprising 20 aggregated categories of goods/materials (Table 2), is in use since 2008. Table 2 demonstrates NST code for main NST categories with example for second level of disaggregation for the category 03 “Metal ores and other mining and quarrying products; peat; uranium and thorium”.

1 http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:NST

8

However, before 2008 transport statistics were provided, either for the 24 groups of goods, as it was defined by Council Directive 78/546/EEC, or aggregated to 10 chapters, as it was defined by Standard Goods Classification for Transport Statistics/Revised, 19672. Both these categories refer to “Standard goods classification for transport statistics/Revised” and abbreviated as NST/R or NST/R 24 and NST/R (1967). Relation between those two classifications is provided in Table 3.

Since some countries might have deeper levels of NST disaggregation, this shall be checked by partners.

Table 2. Standard goods classification for transport statistics, 2007 (NST 2007)3

Code Details 01 Products of agriculture, hunting, and forestry; fish and other fishing products 02 Coal and lignite; crude petroleum and natural gas 03 Metal ores and other mining and quarrying products; peat; uranium and

thorium 03.1 Iron ores 03.2 Non-ferrous metal ores (except uranium and thorium ores) 03.3 Chemical and (natural) fertilizer minerals 03.4 Salt 03.5 Stone, sand, gravel, clay, peat and other mining and quarrying products n.e.c. 03.6 Uranium and thorium ores

04 Food products, beverages and tobacco 05 Textiles and textile products; leather and leather products 06 Wood and products of wood and cork (except furniture); articles of straw and

plaiting materials; pulp, paper and paper products; printed matter and recorded media

07 Coke and refined petroleum products 08 Chemicals, chemical products, and man-made fibers; rubber and plastic

products; nuclear fuel 09 Other non-metallic mineral products 10 Basic metals; fabricated metal products, except machinery and equipment 11 Machinery and equipment n.e.c.; office machinery and computers; electrical

machinery and apparatus n.e.c.; radio, television and communication equipment and apparatus; medical, precision and optical instruments; watches and clocks

12 Transport equipment 13 Furniture; other manufactured goods n.e.c. 14 Secondary raw materials; municipal wastes and other wastes 15 Mail, parcels 16 Equipment and material utilized in the transport of goods 17 Goods moved in the course of household and office removals; baggage and

articles accompanying travellers; motor vehicles being moved for repair; other non-market goods n.e.c.

2 http://ec.europa.eu/eurostat/ramon/nomenclatures/index.cfm?TargetUrl=LST_NOM_DTL&StrNom=NSTR_1967&StrLanguageCode=EN&IntPcKey=&StrLayoutCode=HIERARCHIC 3 http://ec.europa.eu/eurostat/ramon/nomenclatures/index.cfm?TargetUrl=LST_NOM_DTL&StrNom=NST_2007&StrLanguageCode=EN&IntPcKey=&StrLayoutCode=HIERARCHIC

9

Code Details 18 Grouped goods: a mixture of types of goods which are transported together 19 Unidentifiable goods: goods which for any reason cannot be identified and

therefore cannot be assigned to groups 01-16. 20 Other goods n.e.c.

Table 3. NST/R24 vs. NST/R (1967)

NST/R 24 groups of goods NST/R (1967) chapter 1 – Cereals 0 - Agricultural products and live

animals 2 – Potatoes, other fresh or frozen fruit and vegetables 3 – Live animals, sugar beet 4 – Wood and cork 5 – Textiles, textile articles and man-made fibres, other raw animal and vegetable materials 6 – Foodstuffs and animal fodder 1 - Foodstuffs and animal fodder 7 – Oil seeds and oleaginous fruit and fats 8 – Solid mineral fuels 2 - Solid mineral fuels 9 – Crude petroleum 3 - Petroleum products 10 – Petroleum products 11 – Iron ore, iron and steel waste and blast furnace dust

4 - Ores and metal waste

12 – Non-ferrous ores and waste 13 – Metal products 5 - Metal products 14 – Cement, lime, manufactured building materials 6 - Crude and manufactured

minerals, building materials 15 – Crude and manufactured minerals 16 – Natural and chemical fertilizers 7 - Fertilizers 17 – Coal chemicals, tar 8 - Chemicals 18 – Chemicals other than coal chemicals and tar 19 – Paper pulp and waste paper 20 – Transport equipment, machinery, apparatus, engines, whether or not assembled, and parts thereof

9 - Machinery, transport equipment, manufactured articles and miscellaneous articles 21 – Manufactures of metal

22 – Glass, glassware, ceramic products 23 – Leather, textile clothing, other manufactured articles 24 – Miscellaneous articles

Economic activities - NACE Category NACE (from French “Nomenclature générale des Activités économiques dans les Communautés Européennes”) stands for “Statistical classification of economic activities in the European Communities”. Thus, in NACE statistical data is presented by economic activity in the fields of economic statistics (e.g. production, employment, national accounts).

10

The NACE hierarchical structure comprises several levels4:

• a first level consisting of headings identified by an alphabetical code (sections), • a second level consisting of headings identified by a two-digit numerical code

(divisions), • a third level consisting of headings identified by a three-digit numerical code

(groups), • a fourth level consisting of headings identified by a four-digit numerical code

(classes).

Table 4 demonstrates NACE code to up to the second level of disaggregation with example of the fourth level of disaggregation for the division 07 “Mining of metal ores”.

Table 4. NACE Rev.25 (with example of the fourth level of disaggregation for the division 07 “Mining of metal ores”)

Section Division Title A AGRICULTURE, FORESTRY AND FISHING 01 Crop and animal production, hunting and related service activities 02 Forestry and logging 03 Fishing and aquaculture B MINING AND QUARRYING 05 Mining of coal and lignite 06 Extraction of crude petroleum and natural gas 07 Mining of metal ores

• 07.1 Mining of iron ores - 07.10 Mining of iron ores

• 07.2 Mining of non-ferrous metal ores - 07.21 Mining of uranium and thorium ores - 07.29 Mining of other non-ferrous metal ores

08 Other mining and quarrying 09 Mining support service activities C MANUFACTURING 10. Manufacture of food products 11. Manufacture of beverages 12. Manufacture of tobacco products 13. Manufacture of textiles 14. Manufacture of wearing apparel 15. Manufacture of leather and related products 16. Manufacture of wood and of products of wood and cork, except furniture;

manufacture of articles of straw and plaiting materials 17. Manufacture of paper and paper products 18. Printing and reproduction of recorded media 19. Manufacture of coke and refined petroleum products 20. Manufacture of chemicals and chemical products 21. Manufacture of basic pharmaceutical products and pharmaceutical

preparations 22. Manufacture of rubber and plastic products 23. Manufacture of other non-metallic mineral products 24. Manufacture of basic metals 4 NACE Rev.2 (2008) Statistical classification of economic activities in the European Community 5 NACE Rev.2 is provided as example; data may be reported in earlier NACE editions (Rev.1, Rev.1.1)

11

Section Division Title 25. Manufacture of fabricated metal products, except machinery and equipment 26. Manufacture of computer, electronic and optical products 27. Manufacture of electrical equipment 28. Manufacture of machinery and equipment n.e.c 29. Manufacture of motor vehicles, trailers and semi-trailers 30. Manufacture of other transport equipment 31. Manufacture of furniture 32. Other manufacturing 33. Repair and installation of machinery and equipment D ELECTRICITY, GAS, STEAM AND AIR CONDITIONING SUPPLY 35. Electricity, gas, steam and air conditioning supply E WATER SUPPLY; SEWERAGE, WASTE MANAGEMENT AND

REMEDIATION ACTIVITIES 36. Water collection, treatment and supply 37. Sewerage 38. Waste collection, treatment and disposal activities; materials recovery 39. Remediation activities and other waste management services F CONSTRUCTION 41. Construction of buildings 42. Civil engineering 43. Specialised construction activities G WHOLESALE AND RETAIL TRADE; REPAIR OF MOTOR

VEHICLES AND MOTORCYCLES 45. Wholesale and retail trade and repair of motor vehicles and motorcycles 46. Wholesale trade, except of motor vehicles and motorcycles 47. Retail trade, except of motor vehicles and motorcycles H TRANSPORTATION AND STORAGE 49. Land transport and transport via pipelines 50. Water transport 51. Air transport 52. Warehousing and support activities for transportation 53. Postal and courier activities I ACCOMMODATION AND FOOD SERVICE ACTIVITIES 55. Accommodation 56. Food and beverage service activities J INFORMATION AND COMMUNICATION 58 Publishing activities 59 Motion picture, video and television programme production, sound

recording and music publishing activities 60 Programming and broadcasting activities 61 Telecommunications 62 Computer programming, consultancy and related activities 63 Information service activities K FINANCIAL AND INSURANCE ACTIVITIES 64 Financial service activities, except insurance and pension funding 65 Insurance, reinsurance and pension funding, except compulsory social

security 66 Activities auxiliary to financial services and insurance activities L REAL ESTATE ACTIVITIES 68 Real estate activities M PROFESSIONAL, SCIENTIFIC AND TECHNICAL ACTIVITIES 69 Legal and accounting activities

12

Section Division Title 70 Activities of head offices; management consultancy activities 71 Architectural and engineering activities; technical testing and analysis 72 Scientific research and development 73 Advertising and market research 74 Other professional, scientific and technical activities 75 Veterinary activities N ADMINISTRATIVE AND SUPPORT SERVICE ACTIVITIES 77 Rental and leasing activities 78 Employment activities 79 Travel agency, tour operator and other reservation service and related

activities 80 Security and investigation activities 81 Services to buildings and landscape activities 82 Office administrative, office support and other business support activities O PUBLIC ADMINISTRATION AND DEFENCE; COMPULSORY

SOCIAL SECURITY 84 Public administration and defence; compulsory social security P EDUCATION 85 Education Q HUMAN HEALTH AND SOCIAL WORK ACTIVITIES 86 Human health activities 87 Residential care activities 88 Social work activities without accommodation R ARTS, ENTERTAINMENT AND RECREATION 90 Creative, arts and entertainment activities 91 Libraries, archives, museums and other cultural activities 92 Gambling and betting activities 93 Sports activities and amusement and recreation activities S OTHER SERVICE ACTIVITIES 94 Activities of membership organisations 95 Repair of computers and personal and household goods 96 Other personal service activities T ACTIVITIES OF HOUSEHOLDS AS EMPLOYERS;

UNDIFFERENTIATED GOODS- AND SERVICES-PRODUCING ACTIVITIES OF HOUSEHOLDS FOR OWN USE

97 Activities of households as employers of domestic personne 98 Undifferentiated goods- and services-producing activities of private

households for own use U ACTIVITIES OF EXTRATERRITORIAL ORGANISATIONS AND

BODIES 99 Activities of extraterritorial organisations and bodies

Product level and economic activities - Statistical classification of products by activity (CPA) The Statistical classification of products by activity, CPA, is the classification of products at the level of the EU and designed to categorize products that have common characteristics. CPA is used to create a basis for the production, trade, consumption and transport of products.

13

CPA product categories are related to activities as defined by NACE, thus each CPA product - whether a transportable or non-transportable good or a service - is assigned to one single NACE activity.

CPA has a hierarchical structure with six levels, each identified with a specific code:

- first level: 21 sections (alphabetical code); - second level: 88 divisions (two-digit numerical code); - third level: 261 groups (three-digit numerical code); - fourth level: 575 classes (four-digit numerical code); - fifth level: 1 342 categories (five-digit numerical code); - sixth level: 3 142 subcategories (six-digit numerical code).

More information can be found on Eurostat RAMON website6.

Product level - Harmonized system (HS) The Harmonized commodity description and coding system (or Harmonized System, HS) is a multi-purpose international product nomenclature developed by the World Customs Organization (WCO).

The HS 2007 is made up of about 5,000 commodity groups defined at a six-digit level (with an additional two digits if eight-digit sub-divisions are required).

Example

Section II Vegetable products

0207 Edible vegetables and certain roots and tubers.

07.01 Potatoes, fresh or chilled.

0701.10 - Seed

0701.90 - Other

07.02 Tomatoes, fresh or chilled.

0702.00 Tomatoes, fresh or chilled.

07.03 Onions, shallots, garlic, leeks and other alliaceous vegetables, fresh or chilled.

0703.10 - Onions and shallots

0703.20 - Garlic

0703.90 - Leeks and other alliaceous vegetables

More information can be found on Eurostat website7.

6 http://ec.europa.eu/eurostat/ramon/index.cfm?TargetUrl=DSP_PUB_WELC

14

Product level - The Combined Nomenclature (CN) The UMan model generally uses the Combined nomenclature (CN) for goods classification. CN was established by the Council Regulation (EEC) No.2658/87 in order to meet both the Common Customs Tariff and of the external trade statistics of the European Community (EC).

Each CN subheading shall have an eight-digit code number8:

- the first six digits shall be the code numbers relating to the headings and subheadings of the harmonized system nomenclature (HS);

- the seventh and eighth digits shall identify the CN subheadings. When a heading or subheading of the harmonized system is not further subdivided for EC purposes, the seventh and eight digits shall be ‘00’.

The CN is revised on a yearly basis, resulting in a set of separate CN revisions released for years from 1995 to 2017. Since UMan model utilizes an arbitrary CN version, all other revisions are transformed by the model into the arbitrary nomenclature. This means that the project partners can provide data in any available CN revision as long as it is known the version.

Table 5 comprises CN 2007 codes for the main 21 groups with details up to 8-digit code for section V MINERAL PRODUCTS/Chapter 25/Salt as an example. More information about CN codes can be found via Eurostat RAMON website9.

Table 5. CN 2007 (with example of the eight level of disaggregation for the Chapter 25)

Code Title

I SECTION I - LIVE ANIMALS; ANIMAL PRODUCTS

II SECTION II - VEGETABLE PRODUCTS

III SECTION III - ANIMAL OR VEGETABLE FATS AND OILS AND THEIR CLEAVAGE PRODUCTS; PREPARED EDIBLE FATS; ANIMAL OR VEGETABLE WAXES

IV SECTION IV - PREPARED FOODSTUFFS; BEVERAGES, SPIRITS AND VINEGAR; TOBACCO AND MANUFACTURED TOBACCO SUBSTITUTES

V SECTION V - MINERAL PRODUCTS

25 CHAPTER 25 - SALT; SULPHUR; EARTHS AND STONE; PLASTERING MATERIALS, LIME AND CEMENT

2501 00 Salt (including table salt and denatured salt) and pure sodium chloride, whether or not in aqueous solution or containing added

7 Eurostat Glossary: http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Harmonized_commodity_description_and_coding_system . Eurostat RAMON: http://ec.europa.eu/eurostat/ramon/index.cfm?TargetUrl=DSP_PUB_WELC 8 the Council Regulation (EEC) No.2658/87 9 http://ec.europa.eu/eurostat/ramon/index.cfm?TargetUrl=DSP_PUB_WELC

15

Code Title anti-caking or free-flowing agents; sea water

2501 00 10 - Sea water and salt liquors - Common salt (including table salt and denatured salt) and pure sodium chloride, whether or not in aqueous solution or containing added anti-caking or free-flowing agents

2501 00 31 -- For chemical transformation (separation of Na from Cl) for the manufacture of other products -- Other

2501 00 51 --- Denatured or for industrial uses (including refining) other than the preservation or preparation of foodstuffs for human or animal consumption --- Other

2501 00 91 ---- Salt suitable for human consumption 2501 00 99 ---- Other

2502 00 00 Unroasted iron pyrites

VI SECTION VI - PRODUCTS OF THE CHEMICAL OR ALLIED INDUSTRIES

VII SECTION VII - PLASTICS AND ARTICLES THEREOF; RUBBER AND ARTICLES THEREOF

VIII

SECTION VIII - RAW HIDES AND SKINS, LEATHER, FURSKINS AND ARTICLES THEREOF; SADDLERY AND HARNESS; TRAVEL GOODS, HANDBAGS AND SIMILAR CONTAINERS; ARTICLES OF ANIMAL GUT (OTHER THAN SILKWORM GUT)

IX

SECTION IX - WOOD AND ARTICLES OF WOOD; WOOD CHARCOAL; CORK AND ARTICLES OF CORK; MANUFACTURES OF STRAW, OF ESPARTO OR OF OTHER PLAITING MATERIALS; BASKETWARE AND WICKERWORK

X

SECTION X - PULP OF WOOD OR OF OTHER FIBROUS CELLULOSIC MATERIAL; RECOVERED (WASTE AND SCRAP) PAPER OR PAPERBOARD; PAPER AND PAPERBOARD AND ARTICLES THEREOF

XI SECTION XI - TEXTILES AND TEXTILE ARTICLES

XII

SECTION XII - FOOTWEAR, HEADGEAR, UMBRELLAS, SUN UMBRELLAS, WALKING STICKS, SEAT-STICKS, WHIPS, RIDING-CROPS AND PARTS THEREOF; PREPARED FEATHERS AND ARTICLES MADE THEREWITH; ARTIFICIAL FLOWERS; ARTICLES OF HUMAN HAIR

XIII SECTION XIII - ARTICLES OF STONE, PLASTER, CEMENT, ASBESTOS, MICA OR SIMILAR MATERIALS; CERAMIC PRODUCTS; GLASS AND GLASSWARE

XIV

SECTION XIV - NATURAL OR CULTURED PEARLS, PRECIOUS OR SEMI-PRECIOUS STONES, PRECIOUS METALS, METALS CLAD WITH PRECIOUS METAL, AND ARTICLES THEREOF; IMITATION JEWELLERY; COIN

XV SECTION XV - BASE METALS AND ARTICLES OF BASE METAL

16

Code Title

XVI

SECTION XVI - MACHINERY AND MECHANICAL APPLIANCES; ELECTRICAL EQUIPMENT; PARTS THEREOF; SOUND RECORDERS AND REPRODUCERS, TELEVISION IMAGE AND SOUND RECORDERS AND REPRODUCERS, AND PARTS AND ACCESSORIES OF SUCH ARTICLES

XVII SECTION XVII - VEHICLES, AIRCRAFT, VESSELS AND ASSOCIATED TRANSPORT EQUIPMENT

XVIII

SECTION XVIII - OPTICAL, PHOTOGRAPHIC, CINEMATOGRAPHIC, MEASURING, CHECKING, PRECISION, MEDICAL OR SURGICAL INSTRUMENTS AND APPARATUS; CLOCKS AND WATCHES; MUSICAL INSTRUMENTS; PARTS AND ACCESSORIES THEREOF

XIX SECTION XIX - ARMS AND AMMUNITION; PARTS AND ACCESSORIES THEREOF

XX SECTION XX - MISCELLANEOUS MANUFACTURED ARTICLES

XXI SECTION XXI - WORKS OF ART, COLLECTORS' PIECES AND ANTIQUES

Product level - PRODCOM According to Eurostat definition, “PRODCOM” is the abbreviation for the EU system of production statistics for mining and manufacturing (i.e. excluding services, other than “industrial services”).

Prodcom uses the product codes specified on the Prodcom List, which contains about 3900 different types of manufactured products.

Products are identified by an 8-digit code:

• the first 4-digits are the classification of the producing enterprise given by the Statistical Classification of Economic Activities in the European Community (NACE) and the first six correspond to the CPA.

• the remaining digits specify the product in more detail.

Most product codes correspond to one or more Combined Nomenclature (CN) codes, but some (mostly industrial services) do not.

Table 6 provides PRODCOM list 2014 cuts with an example with up to 8-digit disaggregation for the group 07.10 “Mining of iron ores”. More information can be found on Eurostat RAMON website10.

Table 6. PRODCOM List 2014 cuts (with example of 8-digit disaggregation for the group 07.10 “Mining of iron ores”)

07.10 Mining of iron ores 07.10.10 Iron ores 07.10.10.00 Iron ores and concentrates (excluding roasted iron pyrites) 07.29 Mining of other non-ferrous metal ores 08.11 Quarrying of ornamental and building stone, limestone, gypsum, chalk and slate

10 http://ec.europa.eu/eurostat/ramon/index.cfm?TargetUrl=DSP_PUB_WELC

17

08.12 Operation of gravel and sand pits; mining of clays and kaolin 08.91 Mining of chemical and fertiliser minerals 08.93 Extraction of salt 08.99 Other mining and quarrying n.e.c. 10.11 Processing and preserving of meat 10.12 Processing and preserving of poultry meat 10.13 Production of meat and poultry meat products 10.20 Processing and preserving of fish, crustaceans and molluscs ………. ………. ………. 33.11 Repair of fabricated metal products 33.12 Repair of machinery 33.13 Repair of electronic and optical equipment 33.14 Repair of electrical equipment 33.15 Repair and maintenance of ships and boats 33.16 Repair and maintenance of aircraft and spacecraft 33.17 Repair and maintenance of other transport equipment 33.19 Repair of other equipment 33.20 Installation of industrial machinery and equipment

Agricultural statistics Statistics on crop products are a tool for monitoring and managing the market of crop products.

Current EU statistics on crops include data on various crop products or groups of products linked to:

· cultivated, harvested and production areas, · production, · yields and · agricultural land use.

Current agricultural statistics is covered by Council Regulation 543/2009 that repeals Council Regulation 837/90 and Council Regulation 959/93. Information on the current data classification system is provided in Table 7 (in accordance with the current version of statistics reported to Eurostat11).

Table 7. Agricultural statistic – classification

CODE12 LABEL

C0000 Cereals for the production of grain (including seed)

C1000 Cereals (excluding rice) for the production of grain (including seed)

C1100 Wheat and spelt C1110 Common wheat and spelt C1111 Common winter wheat and spelt

11 Eurostat Handbook for Annual Crop Statistics (Regulation 543/2009 & Gentlemen’s/ESS agreements) 12 Codes are in accordance with revision 2015

18

CODE12 LABEL

C1112 Common spring wheat and spelt C1120 Durum wheat C1200 Rye and winter cereal mixtures (maslin) C1210 Rye C1220 Winter cereal mixtures (maslin) C1300 Barley C1310 Winter barley C1320 Spring barley

C1400 Oats and spring cereal mixtures (mixed grain other than maslin)

C1410 Oats

C1420 Spring cereal mixtures (mixed grain other than maslin)

C1500 Grain maize and corn-cob-mix C1600 Triticale C1700 Sorghum

C1900 Other cereals n.e.c. (buckwheat, millet, canary seed, etc.)

C2000 Rice C2100 Rice Indica C2200 Rice Japonica

P0000 Dry pulses and protein crops for the production of grain (including seed and mixtures of cereals and pulses)

P1100 Field peas P1200 Broad and field beans P1300 Sweet lupins P9000 Other dry pulses and protein crops n.e.c. R0000 Root crops R1000 Potatoes (including seed potatoes) R2000 Sugar beet (excluding seed) Betteraves à sucre (semences non R9000 Other root crops n.e.c. I0000 Industrial crops I1100 Oilseeds

I1110-1130 Rape, turnip rape, sunflower seeds and soya

I1110 Rape and turnip rape seeds I1111 Winter rape and turnip rape seeds I1112 Spring rape and turnip rape seeds I1120 Sunflower seed I1130 Soya I1140 Linseed (oil flax) I1150 Cotton seed I1190 Other oilseed crops n.e.c. I2000 Fibre crops I2100 Fibre flax I2200 Hemp I2300 Cotton fibre I2900 Other fibre crops n.e.c I3000 Tobacco I4000 Hops I5000 ABarcelonatic, medicinal and culinary plants I6000 Energy crops n.e.c. I9000 Other industrial crops n.e.c. G0000 Plants harvested green from arable land G1000 Temporary grasses and grazings G2000 Leguminous plants harvested green G2100 Lucerne

G2900 Other leguminous plants harvested green n.e.c.

G3000 Green maize

G9100 Other cereals harvested green (excluding green maize)

G9900 Other plants harvested green from arable land n.e.c.

V0000 Fresh vegetables (including melons) V1000 Brassicas

19

CODE12 LABEL

V1100 Cauliflower and broccoli V1200 Brussels sprouts V1300 Cabbages V1900 Other brassicas n.e.c.

V2000 Leafy and stalked vegetables (excluding brassicas)

V2100 Leeks V2200 Celery V2300 Lettuces

V2300S Lettuces under glass or high accessible cover

V2400 Endives V2500 Spinach V2600 Asparagus V2700 Chicory V2710 Chicory for fresh consumption V2720 Chicory for processing V2800 Artichokes V2900 Other leafy or stalked vegetables n.e.c.

V3000 Vegetables cultivated for fruit (including melons)

V3100 Tomatoes V3110 Tomatoes for fresh consumption V3120 Tomatoes for processing

V3100S Tomatoes under glass or high accessible cover

V3200 Cucumbers

V3200S Cucumbers under glass or high accessible cover

V3300 Gherkins V3410 Eggplants V3420 Courgettes and marrows V3430 Gourds and pumpkins V3510 Muskmelons V3520 Watermelons V3600 Peppers (capsicum)

V3600S Peppers (capsicum) under glass or high accessible cover

V3900 Other vegetables cultivated for fruit n.e.c. V4000 Root, tuber and bulb vegetables V4100 Carrots V4210 Onions V4220 Shallots V4300 Beetroot V4400 Celeriac V4500 Radishes V4600 Garlic

V4900 Other root, tuber and bulb vegetables n.e.c.

V5000 Fresh pulses V5100 Fresh peas V5200 Fresh beans V5900 Other fresh pulses n.e.c. V9000 Other fresh vegetables n.e.c. S0000 Strawberries

S0000S Strawberries under glass or high accessible cover

U1000 Cultivated mushrooms U1100 Champignons U1900 Other cultivated mushrooms n.e.c. H0000 Permanent crops for human consumption

F0000 Fruits, berries and nuts (excluding citrus fruits, grapes and strawberries)

F1000 Fruits from temperate climate zones F1100 Pome fruits F1110 Apples F1111 Apples for fresh consumption

20

CODE12 LABEL

F1112 Apples for processing F1120 Pears F1121 Pears for fresh consumption F1122 Pears for processing F1190 Other pome fruits n.e.c. F1200 Stone fruits F1210_1220 Peaches and nectarines F1210 Peaches F1220 Nectarines F1212_1222 Peaches and nectarines for processing F1230 Apricots F1240 Cherries F1241 Sour cherries F1242 Sweet cherries F1250 Plums F1290 Other stone fruits n.e.c.

F2000 Fruits from subtropical and tropical climate zones

F2100 Figs F2200 Kiwis F2300 Avocados F2400 Bananas

F2900 Other fruits from subtropical and tropical climate zones n.e.c.

F3000 Berries (excluding strawberries) F3100 Currants F3110 Blackcurrants F3120 Redcurrants F3200 Raspberries F3300 Blueberries F3900 Other berries n.e.c. F4000 Nuts F4100 Walnuts F4200 Hazelnuts F4300 Almonds F4400 Chestnuts F4900 Other nuts n.e.c. T0000 Citrus fruits T1000 Oranges T1100 Navel oranges T1200 White oranges (blancas) T1300 Blood oranges (sanguines) T1900 Others oranges n.e.c. T2000 Small citrus fruits T2100 Satsumas T2200 Clementines

T2900 Other small citrus fruits (including hybrids) n.e.c.

T3000 Lemons and acid limes T3100 Yellow lemons T3200 Acid limes T4000 Pomelos and grapefruit T9000 Other citrus fruits n.e.c. W1000 Grapes W1100 Grapes for wines

W1110 Grapes for wines with protected designation of origin (PDO)

W1120 Grapes for wines with protected geographical indication (PGI)

W1190 Grapes for other wines n.e.c. (without PDO/PGI)

W1200 Grapes for table use W1300 Grapes for raisins W1900 Grapes for other purposes n.e.c. O1000 Olives

21

CODE12 LABEL

O1100 Olives for table use O1910 Olives for oil

H9000 Other permanent crops for human consumption n.e.c.

According to Eurostat, the annual data on production refer to the harvested production. The harvest year is the calendar year in which the harvest process starts. The table below visualizes the definition of harvested production.

Biological (real) production Harvested production

Usable production Marketed

production Direct

consumption On-holding losses

and wastage Harvesting

losses Non-harvested

Thus, according to Eurostat annual production statistics comprise 'harvested' agricultural production including on-holding losses and wastage, quantities consumed directly on the farm and marketed quantities, (market) losses during transport, storage and packaging. Usable production, as defined above, is the production to be used in supply balance sheets.

Inventory of data sources to collect In general terms, data collected in the national/local statistical office can be provided with more detail, therefore, all data that needs to be collected from the local/national source. In cases where data is not possible to obtain, then Eurostat website can be used to complement missing data.

1. Road Transport 1.1. International Road Transport 1.1.1. Loaded and unloaded goods by <NUTS 3> per NST category

Since Eurostat does not provide data for this category, the request shall be addressed to the national or local statistical office, or databases.

Table 1.1 demonstrates an example of data which should be provided for international road transport with currently used NST (2007). Information shall be provided for the deepest possible level of NST disaggregation (or alternatively with the deepest disaggregation following the country own system). The dataset shall be complemented by information about data source and data reliability, including the distribution function, confidence intervals, type of data uncertainty.

It should be noticed that until 2007 the data are more likely provided in NSTR24 category.

Table 1.1. International road transport: Loaded and unloaded goods by <NUTS 3> per NST categories

22

NST category

AVAILABILITY

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

Loaded goods 01

01.1

01.2

01.3

……….

02

03

……….

20

Unloaded goods 01

01.1

01.2

01.3

……….

02

03

……….

20

1.2. National Road Transport 1.2.1. Loaded and unloaded goods by <NUTS 3> per NST category

The data for NUTS 3 level detail is available from Eurostat database13.

2. Rail Transport 2.1. International Rail Transport 2.1.1. Amount of goods loaded and unloaded by <NUTS 3> per NST category

Since the data is not available via Eurostat database, request shall be addressed to the national or local statistical office. A datasheet for this category is provided in Table 2.1. Information shall be provided by the deepest possible level of NST disaggregation (or alternatively with the deepest disaggregation following the country own system). The dataset shall be complemented by information about data source and data reliability (including the distribution function, confidence intervals, type of data uncertainty).

It should be noticed that until 2007 the data is more likely provided in NSTR24 category, while after 2007 the data is more probably presented in the currently used NST (2007) category.

13 http://ec.europa.eu/eurostat/data/database

23

Table 2.1. International rail transport: Loaded and unloaded goods by <NUTS 3> per NST category

NST Category

YEAR

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

Loaded goods 01

01.1

01.2

01.3

……….

02

03

……….

20

Unloaded goods 01

01.1

01.2

01.3

……….

02

03

……….

20

2.2. National Rail Transport 2.2.1. Amount of goods loaded and unloaded in <NUTS 3> per NST category

Since data is not available via Eurostat database, request shall be addressed to the national or local statistical office, or databases. A datasheet for this category is provided in Table 2.2. Information shall be provided for the deepest possible level of NST disaggregation (or alternatively with the deepest disaggregation following the country own system). The dataset shall be complemented by information about data source and data reliability (including the distribution function, confidence intervals, type of data uncertainty).


Table 2.2. National rail transport: Loaded and unloaded goods in <NUTS 3> per NST category

NST Category YEAR

24

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

Loaded goods 01

01.1

01.2

01.3

……….

02

03

……….

20

Unloaded goods 01

01.1

01.2

01.3

……….

02

03

……….

20

3. Water Transport The availability of ports shall be checked by a partner.

3.1. International Water Transport 3.1.1. International goods loaded and unloaded by Ports in <NUTS 3> per NST

category A datasheet for this category is provided in Table 3.1. Information shall be provided by the deepest possible level of NST disaggregation (or alternatively with the deepest disaggregation following the country own system). The dataset shall be complemented by information about data source and data reliability (including the distribution function, confidence intervals, type of data uncertainty).


25

Table 3.1. International water transport: Loaded and unloaded goods by ports in <NUTS 3> per NST category

NST Category

YEAR

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

Loaded goods 01

01.1

01.2

01.3

……….

02

03

……….

20

Unloaded goods 01

01.1

01.2

01.3

……….

02

03

……….

20

3.2. National Water Transport 3.2.1. National goods loaded and unloaded by Ports in <NUTS 3> per NST category

A datasheet for this category is provided in Table 3.2. Information shall be provided by the deepest possible level of NST disaggregation (or alternatively with the deepest disaggregation following the country own system). The dataset shall be complemented by information about data source and data reliability (including the distribution function, confidence intervals, type of data uncertainty).


Table 3.2. National water transport: Loaded and unloaded goods by ports in <NUTS 3> per NST category

NST Category

YEAR

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

Loaded goods 01

26

NST Category

YEAR

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

01.1

01.2

01.3

……….

02

03

……….

20

Unloaded goods 01

01.1

01.2

01.3

……….

02

03

……….

20

4. Air Transport The availability of airports shall be checked by a partner.

4.1. International Air Transport 4.1.1. International goods loaded and unloaded and mail by airports in <NUTS 3> per

NST category Since Eurostat database does not provide information on goods loaded/unloaded in airports per NST category, the data request shall be addresses to the national or local databases, or statistical office. An example of datasheet for this category is provided in Table 4.1. The dataset shall be complemented by information about data source and data reliability (including the distribution function, confidence intervals, type of data uncertainty).


27

Table 4.1. International goods loaded and unloaded and mail by airports in <NUTS 3> per NST category

NST Category

YEAR

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

Loaded goods 01

01.1

01.2

01.3

……….

02

03

……….

20

Unloaded goods 01

01.1

01.2

01.3

……….

02

03

……….

20

4.2. National Air Transport 4.2.1. National goods loaded and unloaded and mail by airports in <NUTS 3> per NST

category Since Eurostat database does not provide information on goods loaded/unloaded in airports per NST category, the data search shall be addresses to the national or local databases, or statistical office. An example of datasheet for this category is provided in Table 4.2. The dataset shall be complemented by information about data source and data reliability (including the distribution function, confidence intervals, type of data uncertainty).


Table 4.2. National goods loaded and unloaded and mail by airports in <NUTS 3> per NST category

NST Category

YEAR

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

Loaded goods 01

28

NST Category

YEAR

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

01.1

01.2

01.3

……….

02

03

……….

20

Unloaded goods 01

01.1

01.2

01.3

……….

02

03

……….

20

5. Oil and gas pipeline transport Three types of products shall be specified, namely:

- Crude oil - Refined oil products - Natural gas

5.1. International transport loaded and unloaded in <NUTS 3> 5.2. National transport loaded and unloaded in <NUTS 3> 5.3. Transit by <NUTS 3>

6. Waste by Economic Activity 6.1. Data available for <NUTS 2>

Since for this category Eurostat does not provide data for NUTS 2 level, request shall be addressed to the regional statistical office, environmental department or local databases. Data shall be provided by NACE category with the deepest possible level of disaggregation (at least up to 2-digit, as it provided in Table 4), for the period 1999 – 2014. The dataset shall be complemented by information about data source and data reliability, where the latter includes the distribution function, confidence intervals, type of data uncertainty.

29

6.2. Data available for <NUTS 3> Since for this category Eurostat does not provide data for NUTS 3 level, request shall be addressed to the regional statistical office, environmental department or local databases. Data shall be provided by NACE category with the deepest possible level of disaggregation (at least up to 2-digit, as it provided in Table 4), for the period 1999 – 2014. The dataset shall be complemented by information about data source and data reliability, where the latter includes the distribution function, confidence intervals, type of data uncertainty.

If data is not available at NUTS 2 or 3, it should be collected at Country level (NUTS 0) with the deepest possible level of NST disaggregation.

7. Municipal waste, WEEE, portable batteries and accumulators, end-of-life vehicles

Waste generated

I Municipal waste

According to Eurostat, this category includes waste produced mainly by households, though similar wastes from commerce, offices and public institutions (schools, hospitals, government buildings), waste from selected municipal services, i.e. waste from park and garden maintenance, waste from street cleaning services are also included. Wastes from agriculture and from industries, waste from municipal sewage network and treatment and municipal construction and demolition waste are not included.

Thus, the category “municipal waste” comprises:

- paper, paperboard and paper products - plastics, glass, metals - food waste - textiles - bulky waste (e.g. white goods, old furniture, mattresses); and - garden waste, leaves, grass clippings, street sweepings, the content of litter containers, and

market cleansing waste, if managed as waste.

II WEEE

Classification for WEEE shall be provided in accordance with Annex IA to Directive 2002/96/EC:

- Large household appliances - Small household appliances - IT & Telecommunication - Consumer equipment - Lighting equipment (excl. 5a) - 5a. Gas discharge lamps - Electrical & electronic tools

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- Toys, leisure & sports equipment - Medical devices - Monitor & control instruments - Automatic dispensers

III Portable batteries and accumulators

The definitions are provided in accordance with Directive 2006/66/EC on batteries and accumulators and waste batteries and accumulators:

(1) ‘battery’ or ‘accumulator’ means any source of electrical energy generated by direct conversion of chemical energy and consisting of one or more primary battery cells (non-rechargeable) or consisting of one or more secondary battery cells (rechargeable);

(2) ‘battery pack’ means any set of batteries or accumulators that are connected together and/or encapsulated within an outer casing so as to form a complete unit that the end-user is not intended to split up or open;

(3) ‘portable battery or accumulator’ means any battery, button cell, battery pack or accumulator that:

(a) is sealed; and

(b) can be hand-carried; and

(c) is neither an industrial battery or accumulator nor an automotive battery or accumulator;

(4) ‘button cell’ means any small round portable battery or accumulator whose diameter is greater than its height and which is used for special purposes such as hearing aids, watches, small portable equipment and back-up power;

(5) ‘automotive battery or accumulator’ means any battery or accumulator used for automotive starter, lighting or ignition power;

(6) ‘industrial battery or accumulator’ means any battery or accumulator designed for exclusively industrial or professional uses or used in any type of electric vehicle;

Classification for portable batteries and accumulators:

W160601 Lead batteries W160601PB Lead content of batteries

W160602 Ni-Cd batteries W160602CD Cadmium content of batteries

W160605 Other batteries and accumulators

IV End-of-life vehicles (ELV)

Information about end-of-life vehicles (ELV) is collected based on the Directive 2000/53/EC. Classification for ELV collected shall be presented in aggregative form. However, information on ELV for waste treatment shall be broken down to the categories as follow:

31

- Total Waste (TOTAL) - Total dismantling and de-pollution (DMDP) - Liquids (excluding fuel) (LoW: 1301 until 1306+1406+160113 until

160115+160121+160122+160199) (LIQ) - End-of-life vehicles: total weight of vehicles exported (EXP) - End-of-life vehicles: tyres (W160103) - End-of-life vehicles: oil filters (W160107) - End-of-life vehicles: other materials arising from depollution (excluding fuel) (LoW:

160108 until 160111+160121) (W1601A) - End-of-life vehicles: metal components (LoW: 160117+160118) (W1601B) - End-of-life vehicles: large plastic parts (W160119) - End-of-life vehicles: glass (W160120) - End-of-life vehicles: other arising from dismantling (LoW: 160122+160199)

(W1601C) - Batteries and accumulators (W1606) - Catalysts (W1608) - Total shredding (W1910) - Ferrous scrap (steel) from shredding (W191001) - Non-ferrous materials (aluminium, copper, zinc, lead, etc.) from shredding

(W191002) - Shredder Light Fraction (SLF) (LoW: 191003+191004) (W1910A) - Other materials arising from shredding (LoW: 191005+191006) (W1910B)

Waste treatment

I Municipal waste

The next ways for municipal waste treatment are considered:

- landfill/disposal - incineration - material recycling - composting and digestion.

II WEEE

WEEE waste treatment under studying is as follow:

- put on the market - collection (from private households; other than from private households; total

collected) - treatment (recovery, reuse and recycling, reused as whole appliances).

III Portable batteries and accumulators

- put on the market

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- waste collected - recycling - input fractions to the recycling process

IV End-of-life vehicles (ELV)

- Generated (GEN) - Reuse (REU) - Recycling (RCY) - Energy recovery (RCV_E) - Recovery (RCV) - Disposal (DSP)

7.1. Data available for <NUTS 2> Both amount of waste generated and waste treated shall be specified for NUTS 2 region. The reported period: 1999 – 2014. The dataset shall be complemented by information about data source and data reliability (incl. distribution function, confidence intervals, type of data uncertainty).

7.2. Data available for <NUTS 3>

Both amount of waste generated and waste treated shall be specified for NUTS 3 region. The reported period: 1999 – 2014. The dataset shall be complemented by information about data source and data reliability (incl. distribution function, confidence intervals, type of data uncertainty).

If data is not available at NUTS 2 or 3, it should be collected at Country level (NUTS 0).

8. Air emissions – CO2 emissions by origin (biomass and fossil fuels)

8.1. Data available for <NUTS 2> For this category Eurostat does not provide data for NUTS 2 level. Request shall be addressed to the regional statistical office, environmental department or local databases. Data shall be specified by CO2 origin for the period 1999 – 2014. The dataset shall be complemented by information about data source and data reliability (incl. distribution function, confidence intervals, type of data uncertainty).

8.2. Data available for <NUTS 3> For this category Eurostat does not provide data for NUTS 3 level. The request shall be addressed to the regional statistical office, environmental department or local databases. Data shall be specified by CO2 origin for the period 1999 – 2014. The dataset shall be

33

complemented by information about data source and data reliability (incl. distribution function, confidence intervals, type of data uncertainty).


9. Sludge (Solid Part) from Waste Water Treatment 9.1. Data available for <NUTS 2>

For this category Eurostat does not provide data for NUTS 2 level. The request shall be addressed to the regional statistical office or environmental department. Data shall be provided for sludges from WWT, including moisture content, for the period 1999 – 2014. The dataset shall be complemented by information about data source and data reliability (incl. distribution function, confidence intervals, type of data uncertainty).

9.2. Data available for <NUTS 3> For this category Eurostat does not provide data for NUTS 3 level. The request shall be addressed to the regional statistical office or environmental department. Data shall be provided for sludges from WWT, including moisture content, for the period 1999 – 2014. The dataset shall be complemented by information about data source and data reliability (incl. distribution function, confidence intervals, type of data uncertainty).


10. Primary Crops, Fodder Crops including grassland harvest, Fruits, Vegetables, Berries, Nuts, Flowers, etc. agricultural production

10.1. Data available for <NUTS 0> Data is available via Eurostat website.

10.2. Data available for <NUTS 3> Since for this category Eurostat does not provide information for NUTS 3 level, the request shall be addressed to the national or local statistical office, or database.

Agricultural statistics shall be delivered based on hierarchy specified by Eurostat14 with the deepest possible level of detail (Table 10.3). The dataset shall be supplemented by information about data source and data reliability (incl. distribution function, confidence intervals, type of data uncertainty).

Since codes in Table 10.3 are given in accordance with Agricultural Statistics Revision 2015, a partner can use earlier revisions for the coding system.

14 Eurostat Handbook for Annual Crop Statistics (Regulation 543/2009 & Gentlemen’s/ESS agreements)

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Table 10.3. Agricultural statistics

CODE LABEL

YEAR

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

C0000 Cereals for the production of grain (including seed)

C1000 Cereals (excluding rice) for the production of grain (including seed)

C1100 Wheat and spelt

C1110 Common wheat and spelt

C1111 Common winter wheat and spelt

C1112 Common spring wheat and spelt

C1120 Durum wheat

C1200 Rye and winter cereal mixtures (maslin)

C1210 Rye

C1220 Winter cereal mixtures (maslin)

……. …….

……. …….

……. …….

……. …….

O1000 Olives

O1100 Olives for table use

O1910 Olives for oil

H9000 Other permanent crops for human consumption n.e.c.

11. Wood: Harvested timber wood and fuel wood 11.1. Data available for <NUTS 0>

Some of the data can be found via statistics reported by Eurostat, however, some of the data for periods 1999 – 2007 and 2014 are not available. This data shall be recovered by partners where it is possible. Request therefore shall be addressed to the national statistical office, or database. The dataset shall be supplemented by information about data source and data reliability (incl. distribution function, confidence intervals, type of data uncertainty).

11.2. Data available for <NUTS 3> Since Eurostat does not provide information for NUTS 3 level, the request shall be addresses to the local or national statistical offices, or databases. The dataset shall be complemented by information about data source and data reliability (incl. distribution function, confidence intervals, type of data uncertainty).

12. Sea and inland fish catch, crustaceans, mollusks and aquatic invertebrates

12.1. Data available for <NUTS 0> For the period 1999 – 2014, data can be found via Eurostat website. Where it is possible, missing data shall be recovered by a partner. The dataset shall be complemented by information about data source and data reliability (incl. distribution function, confidence intervals, type of data uncertainty).

35

12.2. Data available for <NUTS 3> The information shall be specified depending on the status of the ports within the NUTS 3 region.

13. Milk and milk products 13.1. Data available for <NUTS 0>

Data is supplied by Eurostat. Missing data, where it is possible, should be recovered by a partner.

13.2. Data available for <NUTS 3> Since Eurostat does not provide data on milk and milk products by NUTS 3 level, the request shall be addressed to the national or local statistical office, or databases. The figures shall be provided for the period 1999 – 2014. The dataset shall be complemented by information about data source and data reliability (incl. distribution function, confidence intervals, type of data uncertainty).

14. Primary meat production (slaughtering) 14.1. Data available for <NUTS 0>

Data for slaughterings in slaughterhouses and slaughtering other than in slaughterhouses is provided by Eurostat.

14.2. Data available for <NUTS 3> Since Eurostat does not provide data on primary meat production by NUTS 3 level, request shall be addressed to a local statistical office. The figures shall be provided for the period 1999 – 2014. The dataset shall be complemented by information about data source and data reliability (incl. distribution function, confidence intervals, type of data uncertainty).

15. Primary eggs production 15.1. Data available for <NUTS 0>

Statistics are supplied by Eurostat.

15.2. Data available for <NUTS 3> Since Eurostat does not provide data on primary eggs production by NUTS 3 level, the request shall be addressed to a local statistical office or databases. The figures shall be provided for the period 1999 – 2014. The dataset shall be complemented by information

36

about data source and data reliability (incl. distribution function, confidence intervals, type of data uncertainty).

16. Minerals Extraction 16.1. Extraction of metallic minerals (copper, lead, zinc, iron, gold, silver, etc.) 16.1.1. Data available for <NUTS 0>

As it follows from data on local units in mining of metal ores (NACE category B07) provided by Eurostat, [number] units were registered in 2014 in NUTS 0.

The request on extraction of metallic minerals shall be addressed to a reginal statistical office.

The data shall be specified by PRODCOM code with the deepest possible level of disaggregation for the period 1999 – 2014. The dataset shall be complemented by information about data source and data reliability (incl. distribution function, confidence intervals, type of data uncertainty). An example of data representation is provided in Table 16.2.

Table 16.2. Extraction of metallic minerals

PRODCOM code (list 2014) Year 1999 2000 2001 … 2013 2014

07.10 Mining of iron ores 07.10.10 Iron ores 07.10.10.00 Iron ores and concentrates (excluding roasted iron pyrites)

07.29 Mining of other non-ferrous metal ores 07.29.11 Copper ores and concentrates 07.29.11.00 Copper ores and concentrates 07.29.12 Nickel ores and concentrates 07.29.12.00 Nickel ores and concentrates 07.29.13 Aluminium ores and concentrates 07.29.13.00 Aluminium ores and concentrates 07.29.14 Precious metal ores and concentrates 07.29.14.00 Precious metal ores and concentrates 07.29.15 Lead, zinc and tin ores and concentrates 07.29.15.00 Lead, zinc and tin ores and concentrates 07.29.19 Other non-ferrous metal ores and concentrates n.e.c.

07.29.19.00 Other non-ferrous metal ores and concentrates

16.1.2. Data available for <NUTS 3> The request on extraction of metallic minerals shall be addressed to a reginal statistical office. The data shall be specified by NACE category with the deepest possible level of disaggregation (or alternatively, following the country own statistical system). The figures shall be provided for the period 1999 – 2014. The dataset shall be complemented by information about data source and data reliability (incl. distribution function, confidence intervals, type of data uncertainty).

37

16.2. Extraction of non-metallic minerals (limestone, quartz, clay, feldspar etc.) 16.2.1. Data available for <NUTS 0>

As it follows from data on local units in mining of ores (NACE category B08 “Other mining and quarrying”) provided by Eurostat:

- [number] local units were reported in 2014 in NUTS 0 - [number] local units were reported in 2014 in NUTS 1 - [number] local units were reported in 2014 in NUTS 2.

Some of the data on the mining of non-metallic minerals can be found via Eurostat. The missing data, if any, shall be recovered by a partner.

The data shall be provided by PRODCOM code with the deepest possible level of disaggregation for the period 1999 – 2014. The dataset shall be complemented by information about data source and data reliability (incl. distribution function, confidence intervals, type of data uncertainty). An example of data representation is provided in Table 16.3.

Table 16.3. Non-metallic minerals extracted


08.11 Quarrying of ornamental and building stone, limestone, gypsum, chalk and slate

08.11.11 Marble and other calcareous ornamental or building stone

08.11.11.33 Marble and travertine, crude or roughly trimmed

08.11.11.36 Marble and travertine merely cut into rectangular or square blocks or slabs

08.11.11.50 Ecaussine and other calcareous monumental or building stone of an apparent specific gravity ≥ 2,5

08.11.12 Granite, sandstone and other ornamental or building stone

08.11.12.33 Granite, crude or roughly trimmed 08.11.12.36 Granite merely cut into rectangular (including square) blocks or slabs

08.11.12.50 Sandstone 08.11.12.90 Porphyry, basalt, quartzites and other monumental or building stone, crude, roughly trimmed or merely cut (excluding calcareous monumental or building stone of a gravity ≥ 2,5, granite and sandstone)

08.11.20 Limestone and gypsum 08.11.20.30 Gypsum and anhydrite 08.11.20.50 Limestone flux, limestone and other calcareous stone used for the manufacture of lime or cement (excluding crushed limestone aggregate and calcareous dimension stone)

08.11.30 Chalk and uncalcined dolomite 08.11.30.10 Chalk 08.11.30.30 Dolomite, crude, roughly trimmed or

38


merely cut into rectangular or square blocks or slabs (excluding calcined or sintered dolomite, agglomerated dolomite and broken or crushed dolomite for concrete aggregates, road metalling or railway or other ballast) 08.11.40 Slate 08.11.40.00 Slate, crude, roughly trimmed or merely cut into rectangular or square blocks or slabs

…….. …….. …….. 08.99 Other mining and quarrying n.e.c. 08.99.10 Bitumen and asphalt, natural; asphaltites and asphaltic rock

08.99.10.00 Natural bitumen and natural asphalt; asphaltites and asphaltic rocks

08.99.21 Precious and semi-precious stones (excluding industrial diamonds), unworked or simply sawn or roughly shaped

08.99.21.00 Precious and semi-precious stones (excluding industrial diamonds), unworked or simply sawn or roughly shaped

08.99.22 Industrial diamonds, unworked or simply sawn, cleaved or bruted; pumice stone; emery; natural corundum, natural garnet and other natural abrasives

08.99.22.00 Industrial diamonds, unworked or simply sawn, cleaved or bruted; pumice stone; emery; natural corundum, natural garnet and other natural abrasives

08.99.29 Other minerals 08.99.29.00 Other minerals

16.2.2. Data available for <NUTS 3> Request is supposed to be addressed based on information about availability of non-metallic mining enterprises (NACE category B08 “Other mining and quarrying”).

17. Sand and Gravel Extraction 17.1. Data available for <NUTS 0>

As it follows from data on local units in mining of ores (NACE category B08 “Other mining and quarrying”) provided by Eurostat:

- [number] local units were reported in 2014 in NUTS 0 - [number] local units were reported in 2014 in NUTS 1 - [number] local units were reported in 2014 in NUTS 2.

Some of the data on sand and gravel extraction can be found via Eurostat statistics. The missing data, if any, shall be recovered by a partner.

39

The data shall be provided by PRODCOM code with the deepest possible level of disaggregation for the period 1999 – 2014. The dataset shall be complemented by information about data source and data reliability (incl. distribution function, confidence intervals, type of data uncertainty). An example of data representation is provided in Table 17.1.

Table 17.1. Sand and gravel extracted


08.12 Operation of gravel and sand pits; mining of clays and kaolin

08.12.11 Natural sands 08.12.11.50 Silica sands (quartz sands or industrial sands)

08.12.11.90 Construction sands such as clayey sands; kaolinic sands; feldspathic sands (excluding silica sands, metal bearing sands)

08.12.12 Granules, chippings and powder; pebbles, gravel

08.12.12.10 Gravel and pebbles of a kind used for concrete aggregates, for road metalling or for railway or other ballast; shingle and flint

08.12.12.30 Crushed stone of a kind used for concrete aggregates, for road metalling or for railway or other ballast (excluding gravel, pebbles, shingle and flint)

08.12.12.50 Granules, chippings and powder of marble

08.12.12.90 Granules, chippings and powder of travertine, ecaussine, granite, porphyry, basalt, sandstone and other monumental stone

08.12.13 Mixtures of slag and similar industrial waste products, whether or not incorporating pebbles, gravel, shingle and flint for construction use

08.12.13.00 Mixtures of slag and similar industrial waste products, whether or not incorporating pebbles, gravel, shingle and flint for construction use

08.12.21 Kaolin and other kaolinic clays 08.12.21.40 Kaolin 08.12.21.60 Kaolinitic clays (ball and plastic clays)

08.12.22 Other clays, andalusite, kyanite and sillimanite; mullite; chamotte or dinas earths

08.12.22.10 Bentonite 08.12.22.30 Fireclay 08.12.22.50 Common clays and shales for construction use (excluding bentonite, fireclay, expanded clays, kaolin and kaolinic clays); andalusite, kyanite and sillimanite; mullite; chamotte or dinas earths

40

17.2. Data available for <NUTS 3> Since Eurostat does not provide data on extraction of sand and gravel for NUTS 3 level, request shall be addressed to the regional statistical office. Data shall be provided by PRODCOM code with the deepest possible level of disaggregation for the period 1999 – 2014. The dataset shall be complemented by information about data source and data reliability (incl. distribution function, confidence intervals, type of data uncertainty).

18. Fossil and Nuclear Fuels Extraction Mining of uranium and thorium ores refer to NACE code B 07.21. Mining of coal and lignite – NACE code B05. Extraction of crude petroleum and natural gas – NACE code B06.

As it follows from data on local units in mining provided by Eurostat, there are:

- [number] local units in group of B05 in NUTS 0 - [number] local units in group of B05 in NUTS 1 - local units in group of B05 in NUTS 2 – confidential information

- [number] units in group of B06 in 2014 in NUTS 0 - local units in group of B06 in NUTS 1 and NUTS 2 – confidential information

- [number] local units in group of B07 in NUTS 0 - local units in group of B07 in NUTS1 and 2 – confidential information

18.1. Data available for <NUTS 0> Potentially enterprises of B 07.21 (NACE) can operate in NUTS 0. There are also [number] units in NACE category B05 (Mining of coal and lignite) and [number] units in NACE category B06 (Extraction of crude petroleum and natural gas).

Data for the groups of B05 and B06 for NUTS 0 can be found via Eurostat statistic website.

The data for the NACE category of 07.21 (Mining of uranium and thorium ores), if there are enterprises in this group, shall be provided by PRODCOM code with the deepest possible level of disaggregation for the period 1999 – 2014. The dataset shall be complemented by information about data source and data reliability (incl. distribution function, confidence intervals, type of data uncertainty).

18.2. Data available for <NUTS 3> Potentially enterprises belonging to NACE categories of B05, B06 and B07 can operate in NUTS 3 region [information is provided based on analysis of availability of local units]. However, since for this category Eurostat does not provide data for NUTS 3 level, the request shall be addressed to the regional statistical office. Data shall be provided by CN or NACE category with the deepest possible level of disaggregation for the period 1999 – 2014. The

41

dataset shall be complemented by information about data source and data reliability (incl. distribution function, confidence intervals, type of data uncertainty).

An example of data representation is provided in Table 18.2.

Table 18.2. Fossil, nuclear fuels extraction – primary energy production

NACE Rev.2 code Availability Year

1999 2000 2001 … 2013 2014 05 Mining of coal and lignite 05.1 Mining of hard coal 05.10 Mining of hard coal 05.2 Mining of lignite 05.20 Mining of lignite 06 Extraction of crude petroleum and natural gas

06.1 Extraction of crude petroleum 06.10 Extraction of crude petroleum 06.2 Extraction of natural gas 06.20 Extraction of natural gas 07 Mining of metal ores 07.2 Mining of non-ferrous metal ores 07.21 Mining of uranium and thorium ores 08 Other mining and quarrying 08.9 Mining and quarrying n.e.c. 08.92 Extraction of peat

19. Electricity, Gas, Steam, Air conditioning Production and Consumption

19.1. Data available for <NUTS 0> Data for NUTS 0 level can be obtained via Eurostat website.

19.2. Data available for <NUTS 3> For this category Eurostat does not provide data for NUTS 3 level. The request shall be addressed to the national or local statistical office or databases. Data shall be provided by NACE category with the deepest possible level of disaggregation or, alternatively, in the manner similar for NUTS 0 level, for the period 1999 – 2014. The dataset shall be complemented by information about data source and data reliability (incl. distribution function, confidence intervals, type of data uncertainty).

An example of data representation is provided in Table 19.2.

Table 19.2. Electricity, gas, steam, air conditioning production and consumption

NACE Rev.2 code Year 1999 2000 2001 … 2013 2014

35 Electricity, gas, steam and air conditioning supply

42

NACE Rev.2 code Year 1999 2000 2001 … 2013 2014

35.1 Electric power generation, transmission and distribution

35.11 Production of electricity 35.12 Transmission of electricity 35.13 Distribution of electricity 35.14 Trade of electricity 35.2 Manufacture of gas; distribution of gaseous fuels through mains

35.21 Manufacture of gas 35.22 Distribution of gaseous fuels through mains

35.23 Trade of gas through mains 35.3 Steam and air conditioning supply 35.30 Steam and air conditioning supply

20. Industrial Production by CN or by PRODCOM code 20.1. Data available for <NUTS 0>

Eurostat provides some of data by PRODCOM code for the period 1995 – 201515. The missing data, where it is possible, shall be recovered by a partner.

21. International Imports and Exports of Goods by CN code 21.1. Data available for <NUTS 0>

Data shall be provided with the deepest possible level of CN disaggregation (i.e. with 8-digit code). An example is presented in Table 21.1. The access to microdata shall be addressed to the national or local statistical office.

The dataset shall be complemented by information about data source and data reliability (incl. distribution function, confidence intervals, type of data uncertainty).

Table 21.1. International trade: import and export of goods by CN code in NUTS 0

DESCRIPTION

AVAILABILITY

TEMPORAL (YEAR)

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

International Imports of goods by CN code in <NUTS 0>

SECTION I - LIVE ANIMALS; ANIMAL PRODUCTS XXXX XX XX

….

etc. all 8-digit codes under section I

SECTION II - VEGETABLE PRODUCTS XXXX XX XX

….

etc. all 8-digit codes under section II

15 http://ec.europa.eu/eurostat/web/prodcom/data/excel-files-nace-rev.2

43

DESCRIPTION

AVAILABILITY

TEMPORAL (YEAR)

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

SECTION III - ANIMAL OR VEGETABLE FATS AND OILS AND THEIR CLEAVAGE PRODUCTS; PREPARED EDIBLE FATS; ANIMAL OR VEGETABLE WAXES XXXX XX XX

….

etc. all 8-digit codes under section III

SECTION IV - PREPARED FOODSTUFFS; BEVERAGES, SPIRITS AND VINEGAR; TOBACCO AND MANUFACTURED TOBACCO SUBSTITUTES XXXX XX XX

….

etc. all 8-digit codes under section IV

SECTION V - MINERAL PRODUCTS XXXX XX XX

….

etc. all 8-digit codes under section V

SECTION VI - PRODUCTS OF THE CHEMICAL OR ALLIED INDUSTRIES XXXX XX XX

….

etc. all 8-digit codes under section VI

SECTION VII - PLASTICS AND ARTICLES THEREOF; RUBBER AND ARTICLES THEREOF XXXX XX XX

….

etc. all 8-digit codes under section VII

SECTION VIII - RAW HIDES AND SKINS, LEATHER, FURSKINS AND ARTICLES THEREOF; SADDLERY AND HARNESS; TRAVEL GOODS, HANDBAGS AND SIMILAR CONTAINERS; ARTICLES OF ANIMAL GUT (OTHER THAN SILKWORM GUT) XXXX XX XX

….

etc. all 8-digit codes under section VIII

SECTION IX - WOOD AND ARTICLES OF WOOD; WOOD CHARCOAL; CORK AND ARTICLES OF CORK; MANUFACTURES OF STRAW, OF ESPARTO OR OF OTHER PLAITING MATERIALS; BASKETWARE AND WICKERWORK XXXX XX XX

….

etc. all 8-digit codes under section IX

SECTION X - PULP OF WOOD OR OF OTHER FIBROUS CELLULOSIC MATERIAL; RECOVERED (WASTE AND SCRAP) PAPER OR PAPERBOARD; PAPER AND PAPERBOARD AND ARTICLES THEREOF XXXX XX XX …. etc. all 8-digit codes under section X

SECTION XI - TEXTILES AND TEXTILE ARTICLES XXXX XX XX

….

etc. all 8-digit codes under section XI

SECTION XII - FOOTWEAR, HEADGEAR, UMBRELLAS, SUN UMBRELLAS, WALKING STICKS, SEAT-STICKS, WHIPS, RIDING-CROPS AND PARTS THEREOF; PREPARED FEATHERS AND ARTICLES MADE THEREWITH; ARTIFICIAL FLOWERS; ARTICLES OF HUMAN HAIR XXXX XX XX

….

etc. all 8-digit codes under section XII

SECTION XIII - ARTICLES OF STONE, PLASTER, CEMENT, ASBESTOS, MICA OR SIMILAR MATERIALS; CERAMIC PRODUCTS; GLASS AND GLASSWARE XXXX XX XX

….

etc. all 8-digit codes under section XIII

SECTION XIV - NATURAL OR CULTURED PEARLS, PRECIOUS OR SEMI-PRECIOUS STONES, PRECIOUS METALS, METALS CLAD WITH PRECIOUS METAL, AND ARTICLES THEREOF; IMITATION JEWELLERY; COIN XXXX XX XX

….

etc. all 8-digit codes under section XIV

SECTION XV - BASE METALS AND ARTICLES OF BASE METAL

44

DESCRIPTION

AVAILABILITY

TEMPORAL (YEAR)

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

XXXX XX XX

….

etc. all 8-digit codes under section XV

SECTION XVI - MACHINERY AND MECHANICAL APPLIANCES; ELECTRICAL EQUIPMENT; PARTS THEREOF; SOUND RECORDERS AND REPRODUCERS, TELEVISION IMAGE AND SOUND RECORDERS AND REPRODUCERS, AND PARTS AND ACCESSORIES OF SUCH ARTICLES XXXX XX XX

….

etc. all 8-digit codes under section XVI

SECTION XVII - VEHICLES, AIRCRAFT, VESSELS AND ASSOCIATED TRANSPORT EQUIPMENT XXXX XX XX

….

etc. all 8-digit codes under section XVII

SECTION XVIII - OPTICAL, PHOTOGRAPHIC, CINEMATOGRAPHIC, MEASURING, CHECKING, PRECISION, MEDICAL OR SURGICAL INSTRUMENTS AND APPARATUS; CLOCKS AND WATCHES; MUSICAL INSTRUMENTS; PARTS AND ACCESSORIES THEREOF XXXX XX XX

….

etc. all 8-digit codes under section XVIII

SECTION XIX - ARMS AND AMMUNITION; PARTS AND ACCESSORIES THEREOF XXXX XX XX

….

etc. all 8-digit codes under section XIX

SECTION XX - MISCELLANEOUS MANUFACTURED ARTICLES XXXX XX XX

….

etc. all 8-digit codes under section XX

SECTION XXI - WORKS OF ART, COLLECTORS' PIECES AND ANTIQUES XXXX XX XX

….

etc. all 8-digit codes under section XXI

International Exports of goods by CN code in <NUTS 0>

SECTION I - LIVE ANIMALS; ANIMAL PRODUCTS XXXX XX XX

….

etc. all 8-digit codes under section I SECTION II - VEGETABLE PRODUCTS XXXX XX XX ….

etc. all 8-digit codes under section II

SECTION III - ANIMAL OR VEGETABLE FATS AND OILS AND THEIR CLEAVAGE PRODUCTS; PREPARED EDIBLE FATS; ANIMAL OR VEGETABLE WAXES XXXX XX XX

….

etc. all 8-digit codes under section III

SECTION IV - PREPARED FOODSTUFFS; BEVERAGES, SPIRITS AND VINEGAR; TOBACCO AND MANUFACTURED TOBACCO SUBSTITUTES XXXX XX XX

….

etc. all 8-digit codes under section IV

SECTION V - MINERAL PRODUCTS XXXX XX XX

….

etc. all 8-digit codes under section V

SECTION VI - PRODUCTS OF THE CHEMICAL OR ALLIED INDUSTRIES XXXX XX XX

….

etc. all 8-digit codes under section VI

SECTION VII - PLASTICS AND ARTICLES THEREOF; RUBBER AND ARTICLES THEREOF XXXX XX XX

45

DESCRIPTION

AVAILABILITY

TEMPORAL (YEAR)

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

….

etc. all 8-digit codes under section VII

SECTION VIII - RAW HIDES AND SKINS, LEATHER, FURSKINS AND ARTICLES THEREOF; SADDLERY AND HARNESS; TRAVEL GOODS, HANDBAGS AND SIMILAR CONTAINERS; ARTICLES OF ANIMAL GUT (OTHER THAN SILKWORM GUT) XXXX XX XX

….

etc. all 8-digit codes under section VIII

SECTION IX - WOOD AND ARTICLES OF WOOD; WOOD CHARCOAL; CORK AND ARTICLES OF CORK; MANUFACTURES OF STRAW, OF ESPARTO OR OF OTHER PLAITING MATERIALS; BASKETWARE AND WICKERWORK XXXX XX XX

….

etc. all 8-digit codes under section IX

SECTION X - PULP OF WOOD OR OF OTHER FIBROUS CELLULOSIC MATERIAL; RECOVERED (WASTE AND SCRAP) PAPER OR PAPERBOARD; PAPER AND PAPERBOARD AND ARTICLES THEREOF XXXX XX XX

….

etc. all 8-digit codes under section X

SECTION XI - TEXTILES AND TEXTILE ARTICLES XXXX XX XX

….

etc. all 8-digit codes under section XI

SECTION XII - FOOTWEAR, HEADGEAR, UMBRELLAS, SUN UMBRELLAS, WALKING STICKS, SEAT-STICKS, WHIPS, RIDING-CROPS AND PARTS THEREOF; PREPARED FEATHERS AND ARTICLES MADE THEREWITH; ARTIFICIAL FLOWERS; ARTICLES OF HUMAN HAIR XXXX XX XX

….

etc. all 8-digit codes under section XII

SECTION XIII - ARTICLES OF STONE, PLASTER, CEMENT, ASBESTOS, MICA OR SIMILAR MATERIALS; CERAMIC PRODUCTS; GLASS AND GLASSWARE XXXX XX XX

….

etc. all 8-digit codes under section XIII

SECTION XIV - NATURAL OR CULTURED PEARLS, PRECIOUS OR SEMI-PRECIOUS STONES, PRECIOUS METALS, METALS CLAD WITH PRECIOUS METAL, AND ARTICLES THEREOF; IMITATION JEWELLERY; COIN XXXX XX XX …. etc. all 8-digit codes under section XIV

SECTION XV - BASE METALS AND ARTICLES OF BASE METAL XXXX XX XX

….

etc. all 8-digit codes under section XV

SECTION XVI - MACHINERY AND MECHANICAL APPLIANCES; ELECTRICAL EQUIPMENT; PARTS THEREOF; SOUND RECORDERS AND REPRODUCERS, TELEVISION IMAGE AND SOUND RECORDERS AND REPRODUCERS, AND PARTS AND ACCESSORIES OF SUCH ARTICLES XXXX XX XX

….

etc. all 8-digit codes under section XVI

SECTION XVII - VEHICLES, AIRCRAFT, VESSELS AND ASSOCIATED TRANSPORT EQUIPMENT XXXX XX XX

….

etc. all 8-digit codes under section XVII

SECTION XVIII - OPTICAL, PHOTOGRAPHIC, CINEMATOGRAPHIC, MEASURING, CHECKING, PRECISION, MEDICAL OR SURGICAL INSTRUMENTS AND APPARATUS; CLOCKS AND WATCHES; MUSICAL INSTRUMENTS; PARTS AND ACCESSORIES THEREOF XXXX XX XX

….

etc. all 8-digit codes under section XVIII

SECTION XIX - ARMS AND AMMUNITION; PARTS AND ACCESSORIES THEREOF XXXX XX XX

46

DESCRIPTION

AVAILABILITY

TEMPORAL (YEAR)

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

….

etc. all 8-digit codes under section XIX

SECTION XX - MISCELLANEOUS MANUFACTURED ARTICLES XXXX XX XX

….

etc. all 8-digit codes under section XX

SECTION XXI - WORKS OF ART, COLLECTORS' PIECES AND ANTIQUES XXXX XX XX

….

etc. all 8-digit codes under section XXI

22. Population 22.1. Data available for <NUTS 2>

Eurostat supplies data for total number of people living in NUTS 2.

22.2. Data available for <NUTS 3> Eurostat provides data for total number of people living in NUTS 3.

22.3. Data available for municipality level (LAUs Level 2) [Where it is applicable]

Since Eurostat does not supply data for LAUs, the request shall be addressed to the local statistical office or database, or another source of data (e.g. national census). Data on population shall be provided by each municipality within NUTS 3. Reported period: 1999 – 2014. The dataset shall be complemented by information about data source and data reliability (incl. distribution function, confidence intervals, type of data uncertainty).

23. Number of employees per NACE category 23.1. Data available for <NUTS 2>

Since Eurostat database does not comprise data on employees for NUTS 2 level per NACE category, the request shall be addressed to the local or national statistical office or database, or to another source of data (e.g. business register). Data shall be provided by NACE category with the deepest possible level of disaggregation (at least 2-digit disaggregation). Reported period: 1999 – 2014. The dataset shall be complemented by information about data source and data reliability (incl. distribution function, confidence intervals, type of data uncertainty).

It should be noticed that depending on reported year data can be provided in different NACE editions (e.g. Rev.1, Rev 1.1, Rev.2)

47

23.2. Data available for <NUTS 3> Since Eurostat database does not comprise data on employees for NUTS 3 level per NACE code, the request shall be addressed to the local or national statistical office or database. Data shall be provided by NACE category with the deepest possible level of disaggregation (at least 2-digit disaggregation). The reported period: 1999 – 2014. The dataset shall be complemented by information about data source and data reliability (incl. distribution function, confidence intervals, type of data uncertainty).


23.3. Data available for municipality level (LAUs Level 2) [Where it is applicable]

Since Eurostat does not supply data for LAUs, the request shall be addressed to the local statistical office or database.

Data on number of employees shall be provided for each municipality within NUTS 3 by NACE category with the deepest possible level of disaggregation. The reported period: 1999 – 2014. The dataset shall be complemented by information about data source and data reliability (incl. distribution function, confidence intervals, type of data uncertainty).


48

References

Eurostat (2012), Economy-wide Material Flow Accounts (EW-MFA): Compilation Guide 2012 Matthews, E., et al (2000), The Weight of Nations, World Resources Institute Niza, S. (2007), Uma Avaliacão do metabolismo humano da economia portuguesa Rosado et al. (2014), A Material Flow Accounting Case Study of the Lisbon Metropolitan Area using the Urban Metabolism Analyst Model. Journal of Industrial Ecology, Vol.18(1), pp.84-101.

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