Analysis of the Urban Metabolism of European Metropolitan Areas

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Analysis of the urban metabolism of European metropolitan areas

Ana Raquel Quartin Duarte

Thesis to obtain the Master of Science Degree in

Mechanical Engineering

Supervisor: Professor Doctor André Alves Pina

Co-Supervisor: Professor Doctor Samuel Pedro de Oliveira Niza

Examination Comittee

Professor Doctor Mário Manuel Gonçalves da Costa Professor Doctor Fernanda Maria Ramos da Cruz Margarido Professor Doctor André Alves Pina

October 2016

Agradecimentos

Em primeiro lugar, quero agradecer ao André Pina e Samuel Niza pela oportunidade dada de realizar este trabalho, e de todo o apoio e orientação dado durante todo o desenvolver do tema, sempre com disponibilidade, bons conselhos a dar e boa disposição. Agradeço também ao João Patrício pela disponibilização de dados necessários para o desenvolvimento do trabalho.

Aos meus colegas e amigos de faculdade do IST, com especial destaque para a Catarina, Maria, Zé, Manel e Pimenta, o meu agradecimento pelo apoio, as conversas, os desabafos, as longas horas de divertimento, que proporcionaram a minha passagem pela instituição mais aprazível e plantaram uma amizade sólida e por muitos anos.

Agradeço também às minhas melhores amigas Mariana, Nicole e Teté, por estarem ao meu lado nesta viagem desde o início, e por me terem escutado nos piores momentos, apoiado e dado motivação sempre que necessitei. À minha amiga Joana, por ter também acompanhado esta jornada, e ao meu amigo Grilo, que me soube sempre animar em fases de maior stress.

Aos meus pais, agradeço a oportunidade que me deram de poder frequentar a faculdade e tirar um curso superior, tendo sempre escutado, compreendido e dado bons conselhos nos meus momentos mais difíceis. À minha irmã, por me ouvir e por ser, por vezes, minha confidente, em momentos improváveis.

Ao meu primo Emanuel, agradeço ter sido um exemplo seguir na área académica, tendo dado motivação para que seguisse com a formação superior em frente e a terminasse.

Agradeço ao meu avô, Engenheiro Quartin Costa, pois teve uma grande experiência profissional e uma carreira repleta de grandes feitos e fomentou o meu objectivo ser como ele e poder contribuir para a sociedade com a minha formação superior.

Por fim, e não menos importante, agradeço ao Pedro por ser a pessoa que é, ambiciosa e com fortes objectivos traçados, e motivar-me a não desistir e a lutar com todas as minhas forças para atingir as minhas ambições. A tua dedicação e perseverança contagiam, continua assim. Esta tese é também para ti.

Abstract

The continuous growth of population and development of cities is leading to problems of sustainability in cities. As metropolitan areas produce the highest share of gross domestic product within a country, a detailed analysis using the urban metabolism concept must be applied, in order to improve plans for sustainability.

The following work intends to analyze material consumption in detail to 12 European metropolitan areas, applying a methodology developed by Pina et al. (2015), which is based in Urban Metabolism Analyst Model (UMAn). It consists in performing the analysis at national level, with publicly available data, and scaling down the results to a urban context, with the use statistics on share of workers per economic activities within the urban area.

The application of the method resulted in values for DMC between 8.30 and 22.96 tons per capita in 2000, for Liverpool and Porto, respectively, and between 8.32 and 22.02 tons per capita in 2011, for Liverpool and Stockholm. For 2011, non-metallic minerals were found to dominate resource consumption, being responsible for between 19% and 65% of DMC.

The economic sectors with highest material consumption are services between 12% and 54%, exports between 6% and 68% and final consumption between 30% and 45%, for most of the cases, with inputs of fossil fuels, non-metallic minerals and biomass.

Keywords: urban metabolism, domestic material consumption, sustainability, material productivity

Resumo

O contínuo crescimento da população e o desenvolvimento das cidades está a intensificar problemas de sustentabilidade nas mesmas. Considerando que as áreas metropolitanas contribuem para uma grande percentagem do Produto Interno Bruto produzido a nível nacional, é necessário analisar o seu desempenho, utilizando o conceito de metabolismo urbano, de modo a desenvolver planos para permitir o crescimento sustentável da cidade.

O trabalho desenvolvido nesta tese tem como objectivo analisar o consumo de material detalhado em 12 áreas metropolitanas europeias, aplicando a metodologia desenvolvida por Pina et al. (2015), baseada no Modelo de Análise de Metabolismo Urbano. Consiste em proceder a uma análise a nível nacional, utilizando um factor de escala para dimensionar os resultados ao nível urbano.

A aplicação do método resultou em valor de consumo de material doméstico entre 8.30 e 22.96 toneladas per capita em 2000, para Liverpool e Porto, e entre 8.32 e 22.02 toneladas per capita para Liverpool e Estocolmo em 2011. Para o ano de 2011, os minerais não metálicos dominam o consumo de recursos, sendo responsáveis por 19% a 65% do consumo doméstico de material.

Os sectores económicos com maior taxa de consumo de materiais são os serviços, com valores entre 12% e 54%, exportações entre 6% e 68% e consumo final entre 30% e 45%, com contribuições maioritárias de combustíveis fósseis, minerais não metálicos e biomassa.

Palavras-chave: metabolismo urbano, consumo doméstico de material, sustentabilidade, productividade de material

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Index

1. Introduction ...... 1 1.1. Motivation ...... 1 1.2. Objectives ...... 3 1.3. Outline ...... 4 2. State of Art ...... 5 2.1. Urban Metabolism ...... 5 2.2. Material Flow Accounting...... 5 2.3. Urban Metabolism Analyst Model ...... 6 2.4. Other methodologies for assessing urban sustainability ...... 10 2.5. Urban Metabolism analysis of Metropolitan Areas...... 11 3. Methodology ...... 15 3.1. Estimation of material extraction and imports/exports ...... 15 3.2. Allocation of products ...... 16 3.3. Decomposition of products ...... 16 3.4. Calculation of the flow of materials ...... 17 3.5. Downscaling ...... 18 3.6. Limitations ...... 18 3.7. Indicators ...... 19 4. Case Studies ...... 20 4.1. Selected Metropolitan areas ...... 20 4.2. Data collected ...... 22 4.3. Economic sector aggregation ...... 22 4.4. Metropolitan areas economic context ...... 24 5. Results ...... 25 5.1. Methodological validations ...... 25 5.2. Total DMC and DMI ...... 26 5.3. Metropolitan areas in a national context ...... 28 5.4. Comparison of EU Metropolitan areas in 2011 ...... 33 5.5. Evolution of Metropolitan areas from 2000 to 2011 ...... 44 6. Conclusions and Future Work ...... 54 6.1. Conclusions ...... 54 6.2. Future work ...... 55 7. Bibliography ...... 56 8. Appendixes ...... 58 Appendix A - Results for DMC and DMI ...... 58 Appendix B – Levels of disaggregation of Metropolitan Areas...... 60 Appendix C – Validation assessment ...... 61

List of Tables

Table 1-Gaps in urban MFA that are addressed by the UMAn model (Rosado et al., 2013). 7 Table 2-MATCAT Nomenclature (Rosado et al., 2013). 8 Table 3-DMC and DMI for several metropolitan areas (MA). 12 Table 4-Databases and respective data collected. 16 Table 5- Composition of metro areas and population. 21 Table 6 - Data description and sources. 22 Table 7 - Economic sectors disaggregation 23 Table 8 - Results at National Level and comparison with Eurostat. 25 Table 9 - Results obtained and comparison with other studies. 27

List of Figures

Figure 1 - Evolution of European Population between 2000 and 2011 (source: ESPON). 1 Figure 2 - Urban Metabolism main components (source: IN+website). 2 Figure 3-Economy-wide material balance scheme (Eurostat, 2001). 6 Figure 4-UMAn model scheme. 7 Figure 5-UMAn Model with main components and interactions (Rosado et al., 2013). 9 Figure 6 - Distribution of GVA per economic sector. 24 Figure 7 - DMC and DMI of France and correspondent Metropolitan Areas. 28 Figure 8 - DMC and DMI of Germany and correspondent Metropolitan areas. 29 Figure 9 - DMC and DMI of Portugal and correspondent Metropolitan areas. 30 Figure 10 - DMC and DMI of Spain and correspondent Metropolitan area. 31 Figure 11 - DMC and DMI for Sweden and correspondent Metropolitan area. 32 Figure 12 - DMC and DMI of UK and correspondent Metropolitan areas. 33 Figure 13- DMC per Material Category for Metropolitan areas in 2011. 34 Figure 14 – DMI per economic sector for Metropolitan areas in 2011. 35 Figure 15 - Berlin (a) and Frankfurt (b) Material Flows in 2011. 38 Figure 16 - Hamburg (a) and Lille (b) Material Flows in 2011. 39 Figure 17 - Lyon (a) and Paris (b) Material Flows in 2011. 40 Figure 18 - Lisbon (a) and Porto (b) Material Flows in 2011. 41 Figure 19 - Liverpool (a) and Manchester (b) Material Flows in 2011. 42 Figure 20 - Madrid (a) and Stockholm (b) Material Flows in 2011. 43 Figure 21 - DMC of Metropolitan Areas for 2000 and 2011. 44 Figure 22 - DMI for Metropolitan Areas in 2000 and 2011. 45 Figure 23 - Evolution of Material Intensity for Metropolitan Areas between 2000 and 2011. 46 Figure 24 - Fossil Fuels inputs per GDP. 47 Figure 25 - Biomass inputs per GDP. 47 Figure 26 – Chemical and Fertilizers inputs per GDP. 47 Figure 27- Metallic Minerals inputs per GDP. 48 Figure 28 - Non-metallic minerals inputs per GDP. 48 Figure 29 - Other materials inputs per GDP. 48 Figure 30–Agriculture and mining sector inputs per GDP. 49 Figure 31 – Biomass products industry inputs per GDP. 50 Figure 32 - Chemicals and fuels products inputs per GDP. 50 Figure 33 - Construction products industry inputs per GDP. 50 Figure 34 - Metallic products industry inputs per GDP 51 Figure 35 - Machinery and Equipment industry inputs per GDP. 51 Figure 36 - Utilities inputs per GDP. 51 Figure 37 - Construction sector inputs per GDP 52

Figure 38– Services sector inputs per GDP. 52 Figure 39 - Final consumption per GDP. 52 Figure 40 - GFCF inputs per GDP. 53 Figure 41 - Exports inputs per GDP. 53

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List of Abbreviations

AGs – Agriculture statistics BM – Biomass CE – Circular Economy CF – Chemicals and Fertilizers DMC – Domestic Material Consumption DMI – Direct Material Input EU – European Union EW-MFA – Economy-wide Material Flow Accounting FAO – Food and Agriculture Organization of the United Nations FC – Final Consumption FF – Fossil Fuels FIs – Fishery statistics Fs – Forestry statistics GDP – Gross Domestic Product GFCF – Gross Fixed Capital Formation GVA – Gross Value Added HS – harmonized System codes IO – Input-Output tables ISIC – International Standard Industrial Classification of All Economic Activities MA – Metropolitan Areas MATCAT – Material Categories Nomenclature MFA – Material Flow Accounting MM – Metallic Minerals NACE – Statistical Classification of Economic Activities in the European Community NM – Non-metallic minerals NST – Standard Goods Classification for Transport Statistics NUTS – Nomenclature of Territorial Units for Statistics O – Other materials OECD – Organization for Economic Co-Operation and Development QMs – Quarry and Mining statistics UK – United Kingdom UM – Urban Metabolism UMAn – Urban Metabolism analyst UN Comtrade – United Nations Commodity Trade Statistics WIOD – World Input-Output Database

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1. Introduction

1.1. Motivation

In spite of embracing only 14% of the world population, the largest metropolitan economies produce more than of 48% of the global gross domestic product (BI, 2012). As Figure 1 shows, overall population is migrating to large urban areas, while rural areas are facing a depopulation (ESPON, 2014). Due to their large populations, urban regions are the stage of high resource consumption, relying significantly on imports and many times resulting in a wasteful use of these resources (Magdy, 2014).

Figure 1 - Evolution of European Population between 2000 and 2011 (source: ESPON).

Moreover, as metropolitan areas are responsible for most of the economic activity within a country and at international level, it is highly important to study urbanization, which is the main key driver of the increase of productivity and economic growth of cities. This phenomenon has steadily accompanied the development of several societies around the world for the last two centuries. On the one hand, urbanization creates an increase of per capita income, as all high-income countries are between 70% and 80% urbanized (Spencer et al., 2009). On the other hand, this leads to the intensification of materials and energy consumption to build the city and support its inhabitant’s activities. Another important factor is that it is estimated that population will continue to grow, mostly in developing countries. According to UN (2014), since 2007, there are more people

living in urban areas than in rural areas, and global urbanization is expected to turn world population into 67% urban versus 33% rural. So the tendency is to increase and develop the number of cities, amplifying the environmental impacts of these regions (Athanassiadis et al., 2016).

As Lash (1999) mentions, the continuous growths of population and economy, and the dependence of society on natural ecosystems interfere with sustainability. All these factors lead to a rapidly increasing consumption, which is driven by economy, defined by Amann et al. (2002) as a group of activities that extracts and transforms materials, keeping them in society for a certain period of time, after which they disposed them as waste to the environment. This means high quantities of waste production and disposal and, therefore, urbanization faces a significant challenge in trying to maximize its benefits while at the same time it minimizes the associated costs to economy, people and environment.

An economic analysis provides only a description of financial flows, so information about environmental consequences and impact of economic activities is not included. For that reason, it is necessary to find metrics that allow the development of eco-efficient economies, to contribute to urban sustainability (Matthews et al., 2000). A viable solution is to analyze cities by applying the concept of Urban Metabolism (UM), first established by Wolman (1965). In this concept, a city should be considered as an ecosystem, where the metabolic requirements are the construction materials, food, fuel, clothing and energy, used to fulfill all the needs within the urban area. Three steps are part of a generic urban metabolism analysis and are shown in Figure 2:i) perform an urban flows assessment, where material flows and activities of a city are mapped, providing a framework of technical and socioeconomic practices and processes; ii) define the most critical factors for urban sustainability, identifying the waste generation and inefficient activities in it; and iii) develop strategies for resource efficient cities and sustainable plans to implement within its boundaries.

Figure 2 - Urban Metabolism main components (source: IN+website).

From an economic perspective, urban metabolism is able to assess the relation between urban growth and the economy, detecting the most relevant economic activity and analyzing potential cooperation between industries. The urban metabolism concept has three main contributions: benchmarking quantitative data on resource use and waste generation, improve techniques for the development of city typologies and supporting the development of alternative policy scenarios (Ferrão et al., 2014). An urban metabolism profile can be applied to define sustainability indicators, inputs to urban greenhouse gas accounting, develop dynamic mathematical models to simulate changes originated by technology and policies, material flows and stocks, and create design tools to assist sustainability planning (Kennedy et al., 2011).

However, while urban metabolism has been identified as an important concept to assess urban sustainability, only a few studies that quantify resource use in urban areas have been performed, particularly if detailed analyses of material consumption by economic activity are considered.

1.2. Objectives

The main goal of this thesis is to calculate the material consumption of several metropolitan areas, per material type and economic sector, and also to determine the allocation of different material categories per economic activity. The applied method allows the calculation of material consumption and eco-efficiency of urban areas, applying it to a national level and scaling down the results to the regional level. For this purpose, regional statistics data, with a disaggregation by economic activity, are necessary.

The main key questions to answer throughout this thesis are the following:

1. Is it possible to assess material consumption of several urban regions, using the available statistics data?

Among several studies published over the years, there is a lack of standardization in the different methodologies used. Therefore, it is not always easy to derive general conclusions regarding material consumption. One of the main obstacles is the lack of available statistical data, especially at the regional level. Therefore, a methodology that uses national statistics and then uses a scaling factor to obtain results provides an important compromise between applicability and detail. The applied methodology is able to estimate material consumption using regional statistics, mainly the share of workers per economic activity and overall population of the urban area in study.

2. Can resource consumption be discriminated for different material categories and economic sectors?

Extending the answer from the previous key question, the great difficulty is to find statistical data with the level of detail required. In terms of material categories, the applied methodology allows disaggregating

material consumption into 6 main categories based on the MATCAT nomenclature. For economic activities, material flows are allocated into the correspondent sectors, using input-output tables which define a level of economic disaggregation. To perform the analysis, the share of workers must correspond to the level of disaggregation set, using European and national statistics databases.

3. Is it possible to study the evolution of material consumption and productivity of urban areas?

To perform an evolutionary analysis, the methodology described is applied to the selected metropolitan areas to the years2000 and 2011. This allows studying the evolution of material consumption by comparing the results obtained for both years. In this work, the material productivity is assessed based on GDP values and final material consumption, allowing the assessment if metropolitan areas are increasing or decreasing their efficiency and productivity.

1.3. Outline

This thesis is structured in six chapters, as follows. Chapter 1 presents a contextualization of the topic under analysis, highlighting the importance of the theme in study. Chapter 2 addresses the concepts and descriptions of several methodologies previously used to assess urban metabolism, providing an overview of previous studies. Chapter 3 explains in detail the methodology applied in this analysis, which is based in the Urban Metabolism Analyst Model (UMAn). Chapter 4 describes the case studies, which consist of 12 metropolitan areas in Europe, and all the performed assumptions are explained. The results obtained are presented and discussed in Chapter 5.Finally, the main conclusions are presented in Chapter 6and suggestions for further work and studies are discussed.

2. State of Art

In this section, a description of the main concepts and methods used to analyse the metabolism of urban areas is presented.

2.1. Urban Metabolism

As explained in Science for Environment Policy (2015) the concept of urban metabolism is an analogy to the metabolism of an organism. In the same way as living beings require resources and generate waste products, urban areas need energy, materials and water to sustain the community through the production of goods, providence of services and elimination of waste and pollution. The metabolism of cities was first mentioned by Wolman (1965), who sustains that the requests for an urban metabolic cycle include all the materials and commodities necessary to the support the activities of the community at home, work, and places for leisure. These materials include construction materials, food, clothes and fuel, and the commodities include several types of services and energy and water supply. The interaction of urban metabolic cycle with the surrounding environment is also crucial. As describes Costa et al. (2004), “this environment can be considered both as a supporting system for the acquisition of resources, which are necessary to maintain the urban processes and as a “sump”, used to dispose exhausted materials”.

As a consequence of resource consumption and environmental contact, three main metabolic problems emerge in an urban area: provision of sufficient water supply, the effective clearance of sewage and control of air pollution (Wolman, 1965).As such, the analysis of urban metabolism has great importance in order to achieve a sustainable city and the methods applied should be as robust as possible to improve the precision of the results and describe its main characteristics. As discussed by Rosado et al. (2016), several metrics such as Material needs; Accumulation; Dependency; Support; Efficiency; Diversity of Processes; Self- sufficiency and Pressure on the environment; could be considered to characterize an urban area.

One of the most used methods to perform UM analysis is Material Flow Accounting (MFA), which uses the principle of mass balancing. However, there are a limited amount of extensive UM studies (Voksamp et al., 2016), and the various approaches implemented so far follow distinguished purposes and are based in different assumptions (Bringezu, 2000). This lack of unity creates limitations on consolidation and comparison of results, which restrains overall conclusions to improve the sustainability of the urban system (Rosado et al., 2013).

2.2. Material Flow Accounting

Material flow analysis (MFA) is the basis of the majority of the methodologies developed to evaluate urban metabolism. The main principle used in MFA is the first law of thermodynamics, which states that matter is conserved during a physical transformation. Therefore, material and energy balances are performed in order to obtain results.

After Wolman (1965) defined the metabolism of cities and stated the main issues concerning this process, several studies were made and a great amount of MFA projects and analysis were released to the scientific community, according to Bringezu (2000). Based on all previous research, Eurostat finally released a methodological guide of MFA in 2001, named Economy-wide material flow accounts and derived indicators – A methodological guide, which is still one of the most important references in the field nowadays. To perform flow balances, the system boundary and material inputs must be defined. Figure 3presents the scheme of an economy-wide material balance, with the main inputs, outputs and net addition to stock described.

Figure 3-Economy-wide material balance scheme (Eurostat, 2001).

Typically, MFA flow studies only consider the input and output sides. This means that economy-wide MFA does not generally account for flows and balances within the economy. Moreover, the MFA methodology is not standardized for urban level analysis, according to Voskamp et al. (2016). Rosado et al. (2013) further asserts that there are other gaps that limit its application and impacts, such as lack of material flows data at the urban level, limited discrimination of material types and lack of understanding about the flows and added stock.

2.3. Urban Metabolism Analyst Model

The urban metabolism analyst model (UMAn) methodology was developed by Rosado et al. (2013) to attempt to overcome the gaps resultant from the MFA method. Among the several solutions achieved, the division of products into 28 material categories, the disaggregation of flow data by economic sector, the description of the life cycle phase of products and the segregation of import and exports flows are highlighted. Table 1 presented by Rosado et al. (2013) summarizes the main developments of this methodology.

Table 1-Gaps in urban MFA that are addressed by the UMAn model (Rosado et al., 2013).

Gap UMAn developments to bridge the gap 1. Lack of a unified methodology Use of statistical data provided by the OECD and Eurostat to perform urban MFA 2. Lack of material flows data at the urban scale Use of national statistics coupled with regional statistics allows calculation of consumption within metropolitan regions 3. Limited discrimination of material types Use of 28 categories of materials instead of only five, used by standard MFA 4. Limited resolution of consumption by economic Use of national transportation and international trade statistics activity 5. Limited understanding of the origin and destination Characterization of the supply chain of products in order to identify the of flows economic activities involved in manufacture or final products for consumption 6. Lack of understanding about the dynamics of added Measurement of the dynamics of stocks by combining the lifespan plug-in stock database and the throughput description parameter 7. Lack of knowledge about the magnitude of cross Establishment of clear calculation techniques for metropolitan areas and flows decoupling of cross flows from imports and exports

The UMAn methodology is performed by addressing four main components: platform, statistics, plugins and calculator. Some of them have different phases, which are explained in the following paragraphs. Figure 4 shows a scheme of the constituents of the UMAn model.

Calculator: Platform: Statistics: Plugins: products, materials, theoretical framework data necessary for databases of flows MFA indicators, spatial of urban areas calculations disaggregation

Figure 4-UMAn model scheme.

i) Platform It is important to describe in detail the inputs and outputs that must be considered in an urban area, as well as the cross flows, imports and exports that characterize the material flows existing in the metropolitan region. Therefore, it is crucial to clearly define boundaries in order to analyse the dynamics of the flows of the urban area. ii) Statistics

This particular component presents some adversities. As Rosado et al. (2013) states, “one of the major difficulties in quantifying material flows at regional or urban scales has to do with the availability of data at the established boundaries”. Some entities, as Eurostat and national statistic offices, can be a useful resource to collect the necessary data, such as forestry (Fs), quarry and mining (QMs), agriculture (AGs) and fishery (FIs) statistics. The Standard Goods Classification for Transport Statistics (NST) is also a good source of information and provides data according to Eurostat’s Nomenclature of Territorial Units for Statistics (NUTS) 2 level (explained in detail in section 2.5).

iii) Plug-ins

This constituent is formed by three distinct databases, generated from the data collected. These databases are the following:

1. Material composition –as previously mentioned, materials are categorised in 28 different categories, according to the material categories nomenclature (MATCAT). This classification was developed by Rosado et al. (2013) with the purpose of detailing material flows and analyse what types of materials can be recovered from products. Table 2presents the material categories considered in the MATCAT nomenclature. In addition to this database, a material composition matrix (ProdChar) that characterizes products in terms of their composition was also developed by Rosado.

Table 2-MATCAT Nomenclature (Rosado et al., 2013).

FF1 Low ash Fuels FF2 High ash Fuels Fossil Fuels (FF) FF3 Lubricants and Oils and Solvents FF4 Plastics and Rubbers MM1 Iron, Steel Alloying Metals and Ferrous Metals MM2 Light Metals Metallic MM3 Non-Ferrous Heavy Metals Minerals (MM) MM4 Special Metals MM5 Nuclear Fuels MM6 Precious Metals NM1 Sand NM2 Cement Non-metallic NM3 Clay minerals (NM) NM4 Stone NM5 Other (Fibers, Salt, inorganic parts of animals) BM1 Agricultural Biomass BM2 Animal Biomass Biomass BM3 Textile Biomass (forestry, crops BM4 Oils and Fats and animal BM5 Sugars products) (BM) BM6 Wood BM7 Paper and Board BM8 Non-Specified biomass CF1 Alcohols Chemicals and CF2 Chemicals and Pharmaceuticals Fertilizers (CF) CF3 Fertilizers and Pesticides O1 Non-Specified Others (O) O2 Liquids

2. Product Lifespan – this database was created in order to understand the capacity of products to be reused or recycled, as well as their availability as resources in the future.

3. Life Cycle Phase –To evaluate the life cycle of products, from raw materials until final goods, and make it possible to estimate the waste and the emissions resultant from their use and disposal, a database was created to characterize to which life cycle phase did each product belong to.

iv) Calculator

This last component allows the calculation of the indicators necessary to perform the analysis through four steps: materials accounting, throughput over time, distribution by economic activity and spatial distribution. These processes compute mass flow balances to conclude how much is consumed, extracted, imported and export, how long do the products last, what are the economic sectors that consume more material and how it is the distributed in the urban area.

Figure 5 summarizes the entire implementation of UMAn model, with all the components.

Figure 5-UMAn Model with main components and interactions (Rosado et al., 2013).

The Urban metabolism analyst model (UMAn) was applied to Lisbon Metropolitan area in Rosado et al. (2013). The results obtained were found to be consistent with previews analysis, with differences around 3,3%.

2.4. Other methodologies for assessing urban sustainability

Other ideologies and concepts are proposed by several researchers to provide an accurate urban analysis. They consider different spatial, temporal, economic, social and technical aspects.Three other methodologies are described in the following paragraphs: circular economy, which has been the principal method used to assess urban areas in China, energy and emergy analysis, that provide a more detailed perspective in terms of environment and energy consumption, and classification tree analysis.

Circular Economy

The Circular Economy (CE) methodology was first implemented in Germany, in 1996, and is being developed by the central government of China since 2002 in order to increase the energy and resources efficiency in several urban areas. According to Su et al. (2013), this concept was first proposed by two British environmental economists Parker and Turner, in 1990, where they point out the necessity of evaluate economy resources as a closed system. This allows relating the raw materials and used resources with waste production, under the first law of thermodynamics, and explore the possibility of reduction, reuse and recycle.

As explained by Su et al. (2013), CE implementation occurs at three different levels (micro, meso and macro-level), in four main areas: production area, consumption area, waste management area and other support. This aims to motivate producers to practice a cleaner production and develop an eco-design policy, create eco-industrial parks and improve the co-operative networks between several industries.

The main advantages of circular economy (CE) are highlighted by Gen et al. (2012), where it is mentioned that China’s main environmental concern is to pursue a “green image”. As such, indicators given by CE are important to improve the ecologic efficiency of the country through the conservation of natural resources and minimization of waste disposal, and provide potential social benefits, as health level, social interaction between industries and employment opportunities. However, there is a gap in the acquaintance of social, industrial/urban interaction, business and material and energy reduction indicators. At the implementation level, the method is not standardized and each local authority is responsible to collect the necessary data and calculate the indicators, leading sometimes to the manipulation of the results for political reasons.

Energy/Emergy

Emergy synthesis method is applied based on the definition of urban metabolism: considering the city as an organism, emergy is used to quantify material and energy flows, as well ecological-socioeconomic systems, as mentioned by Lei et al. (2016). According to them, emergy synthesis was firstly proposed by Odum in 1988 and represents the amount of energy necessary to produce a good or service, accounting energy perseveration and losses during the production process. In emergy analysis, energy and matter flows are multiplied by its respective solar transformity, which is defined by Zhang et al. (2009) as “the quantity of one type of emergy required to generate a unit of energy of

another type”. Each product or service has its own transformity and they were calculated by Odum and several colleagues in order to perform this analysis. As consequence of this method, some indicators can be calculated and the urban metabolism of the area selected can be analysed. Zhang et al. (2009) present the metabolic factors to assess the urban metabolism:

 Flux – Describes the amount of mass and energy within the system and imported from the exterior;  Structure – Accounts the metabolic structures of renewable and non-renewable sources, and of local and external resources;  Intensity – Translates the income and living standards of the people who live in the urban area;  Efficiency – Describes the resource utilization efficiency of the urban area;  Density – Indicates the environmental pressure that urban metabolism generates per unit of urban area.

Although very useful to assess the environmental status of an urban area, the emergy synthesis method does not perform an economic analysis, diverging from the purpose of the previous methods described.

Classification trees

Férnandez et al. (2016) developed a methodology that uses classification trees to estimate resource consumption, from variables as climate, city GDP and population. Each tree has its own decision rules for major categories of material and energy resources, providing a starting point for additional investigation on the area.The results obtained in this study allowed the creation of urban typologies based on the characteristics of the urban areas considered.

2.5. Urban Metabolism analysis of Metropolitan Areas

As previously mentioned, the first crucial step to perform urban metabolism analyses consists in defining the most suitable boundaries, in order to be able to compare results with other case studies, and accurately define import and exports flows within an urban area. Eurostat created a system to divide the economic territory in the European Union (EU) in order to simplify data collection, standardize European regional statistics, and allow balanced socio-economic analyses of the regions. This division is named nomenclature of territorial units for statistics (NUTS) and is divided into three categories:  NUTS 1: great socio-economic regions;  NUTS 2: basic regions for the application of regional policies;  NUTS 3: small regions for specific diagnoses.

The latest definition is the NUTS 2013 classification, valid from 1 January 2015.Metropolitan regions are considered based on these definitions. Eurostat establishes that metro regions are a single or an agglomeration

of NUTS 3 regions with more than 250 000 inhabitants. This classification differentiates three types of metropolitan regions: capital city regions, second-tier metro regions and smaller metro regions. The first type are the regions that include the capital of the country, the second one are the largest cities that do not include the capital, and the last type are minor areas as the names indicates. Most of urban metabolism analysis are performed in metropolitan regions, as it is the case of Hamburg (Hammer et al., 2003; Hammer &Giljum, 2006), Lisbon (Rosado et al., 2013; Pina et al., 2015), Madrid (Sastre et al., 2015), Paris (Barles, 2009; Pina et al., 2015), and Stockholm (Rosado et al., 2016; Kalmykova et al., 2016), which are in accordance with the Eurostat’s definition. However, Niza et al. (2009) mapped the metabolism profile of Lisbon city, which is smaller than Lisbon metropolitan area. Therefore, comparisons should be done carefully and considering the different region boundaries.

Beside the choice of the most adequate geographical boundaries, the methodology used is also relevant to the analysis. The MFA method is mostly used to map profiles at the regional level, as executed in regions as Hamburg (Hammer et al., 2003; Hammer &Giljum, 2006), Lisbon (Niza et al., 2009), Paris (Barles, 2009) and Stockholm (Kalmykova et al., 2016).Some adaptations or models based on MFA are also common to find, especially EW-MFA method, used by Sastre et al. (2015) to assess the Autonomous Community of Madrid, and UMAn, applied by Kalmykova et al. (2015) to Stockholm and by Rosado et al. (2013) to the Lisbon Metropolitan Area. The necessity of adaptation or application of other methodologies leads to an inconsistency in urban metabolism analysis.

Several studies about urban metabolism were published over the years.Table 3 summarizes outputs indicators such as domestic material consumption (DMC) and direct material input (DMI) for several urban areas, obtained with different methods or adaptations, which will be used to compare the results obtained in this thesis. As some articles focus only domestic consumption and other indicators, values for DMI are not available for all the cases.

Table 3-DMC and DMI for several metropolitan areas (MA).

Region Method DMC[t/cap] DMI[t/cap] year Source Hamburg MA MFA 11.00 65.00 2000 Hammer &Giljum (2006) City of Lisbon Adapted MFA 20.08 20.41 2004 Niza et al. (2009) UMAn 10.76 - 2004 Rosado et al. (2013) Lisbon MA Streamlined Urban Metabolism 17.10 18.90 2000 Pina et al. (2015)

Streamlined Urban Metabolism 15.50 17.60 2000 Pina et al. (2015) Paris MA MFA 7.10 12.30 2003 Barles (2009) MFA 10.34 12.12 2011 Rosado et al. (2016) Stockholm MA UMAn 10.10 16.30 2011 Kalmykova et al. (2015) Madrid MA EW-MFA 5.90 - 2010 Sastre et al. (2015)

As shown in Table 3,values of DMC vary between 5.90 and 20.08 tons per capita, whereas DMI presents a range of values between 12.12 to 65.00 tons per capita. Overall differences between DMI and DMC results represent the quantity of exports of that urban area. As explained, several studies have been published over the years using different approaches to assess the urban metabolism of cities. In 2006, Hammer & Giljum assessed the Hamburg metro area for the year of 2000, using the Material Flow Analysis method. The analysis was made based on environmental statistics, such as raw material and water consumption, and waste and emissions data, to account for indicators such as material consumption and material intensity. The goal was to complement the environmental aspects with an economic overview. Relying on this method, a domestic material consumption (DMC) of 11 tons per capita and a direct material input (DMI) of 65 tons per capita were estimated. The notorious difference between these values is due to the fact that Hamburg has a harbor which is a major gateway for the entrance of materials in Europe, which are then relocated to other regions. In this work, the authors consider all the materials entering the harbor as an input to the urban area, therefore being account for in DMI, but then also consider that they are exported to other locations, which thus results in a much lower DMC. Niza et al. (2009) present a study of the city of Lisbon in 2004 with an adaptation of the MFA method. With the construction of several matrices, and using data on international trades, transport statistics, purchasing power, industrial production and waste production, the authors estimate a DMC and DMI of 20.08 and 20.41 tons per capita, respectively. In this case, the regional boundaries used correspond to a smaller area than the Lisbon metropolitan area. Concerning the whole metropolitan area of Lisbon, two methodologies were applied by two different authors, for distinguished years: Urban Metabolism Analyst model (Rosado et al. 2013), for 2005, and the streamlined UM model (Pina et al. 2015), for 2000. The first model uses statistical data on industrial production, international trade, socio-economic indicators as number of workers per economic activity to assess material consumption and economic and spatial distribution, resulting in a DMC of 10.76 tons per capita. The second method, which is the one used in this work, is based on input-output tables, domestic extraction, international trade and number of workers statistics and estimated a DMC and DMI values of 17.1 and 18.9 per capita, respectively.

In the same study performed by Pina et al. (2015), the Paris metro area was also assessed, and the results were 15.5 tons per capita for DMC and 17.6 tons per capita for DMI. These results contrast with the ones presented in Barles (2009), who performed an UM analysis of the Paris metropolitan area in 2003 following the MFA method defined by Eurostat. With data of local extraction of biomass, data on imports and exports, and data on household wastes, the results were 7.1 and 12.3 tons per capita of DMC and DMI, respectively. One of the main assumptions made by Barles (2009) is that waste is part of the exports, which may contribute to the much lower values of DMC obtained in the analysis.

For the Stockholm metropolitan area, two studies were released, one by Kalmykova et al. (2015) and another by Rosado et al. (2016), both for 2011. The first study used the UMAn model, as presented in Rosado et al. (2013), estimating a DMC and DMI of 10.1 and 16.3 tons per capita, respectively. The second study applied the traditional MFA method, obtaining results of 10.34 and 12.12 tons per capita for DMC and DMI, respectively. Sastre et al. (2015) applied the economy-wide MFA method to Madrid for the year 2010,

presenting a result of 5.9 tons per capita of DMC. One of the reasons for this low value may be the fact that this study did not include railway flows.

In order to be able to perform a more consistent comparison of the results obtained for each urban area, this work applies a single methodology to a variety of case studies.The assessment of the twelve metropolitan regions in this work follows the method developed by Pina et al. (2015), presented in detail in the next chapter. This methodology was chosen as the application of the MFA method requires detailed statistics to assess the material consumption in an urban area, such as international trade statistics, transport statistics and other production statistics of several economic sectors. As some of the data may not be available at the regional level, especially transport statistics, this UM model allows overcoming these types of constraints using monetary input-output tables and downscaling factors.

The collection of data is an issue that creates great obstacles to perform the same type of analysis on different urban areas. As consequence of the lack of relevant data, methodologies tend to be developed according to what type of information is available. The lack of unity in methodologies is also evident in some analysis, when two different methods are applied to the same case study with very different results, as is the case for Paris with the works by Pina et al. (2015) and Barles (2009). One of the possible reasons for this discrepancy is due to the fact that Barles (2009) considers waste as exports, while the same is integrated in the DMC calculated by Pina et al. (2015).Nevertheless, as no reliable and consistent data is available regarding the consumption of materials in urban areas, previous urban metabolism studies are important to provide a comparison for the results obtained in this work, as well as to also understand and recognize the limitations of the applied method.

3. Methodology

The urban metabolism analysis of European metropolitan areas was performed with the UM model proposed by Pina et al. (2015), which is based on the UMAn model previously described and developed by Rosado et al. (2013). In an urban economy, the flows considered are:  Inputs, such as domestic extraction and imports;  Consumption of materials and products;  Addition to stock, which is the accumulation of materials in a system; and  Outputs, which consist of exports, emissions and wastes. The need for adapting the previously developed methodology comes from the limitations existing on UMAn, namely the difficulty on getting statistic data for different urban areas such as transport statistics. As Pina et al. (2015) explain, the lack of these statistics will create a barrier to savvy the imported raw materials and products in the selected area. As such, this proposed UM model was based on national or regional monetary input-outputs (IO) tables, available in national statistics offices in most countries. These tables describe the flows between 37 economic sectors and with households and government. This new method presents five sequential steps that are described in detail by Pina et al. (2015): 1. Estimating domestic extraction and imports/exports; 2. Allocation of the products and material to the economic sector that produce them; 3. Decomposition of products and materials to 28 material categories; 4. Calculation of the flow of materials across economic sectors through IO tables and estimation the mass content for each material and sector (in kg/monetary unit); 5. Downscaling the results to the urban area, using scaling factors.

Each one of these steps is described in detail in the following sections.

3.1. Estimation of material extraction and imports/exports

This first step consists on collecting statistical data referring to domestic extraction and trades. The most reliable databases are Eurostat, OECD, WIOD, Material Flows website, Food and Agriculture Organization of the United Nations website and United Nations Commodity Trade Statistics database (UN Comtrade), present by Pina et al. (2015). Table 4 describes what type of information can be collected from each source.

Table 4-Databases and respective data collected.

Database Information Eurostat Material Flows Allows to collect data of domestic extraction and trade statistics (FAO) OECD Organization with Input-output tables UN Comtrade Main source to gather data about imports and exports

Regarding domestic extraction, the information collected from these databases is then categorized into 268 raw materials, according to the harmonized system codes (HS) from the 1996 revision. Other nomenclatures can be used, such as standard international trade classification or the economy-wide MFA. Data on imports/exports can be collected from UN Comtrade, where monetary values and weight of each product traded between two countries are reported.

3.2. Allocation of products

The allocation of products consists in assigning the materials and products categorized in the first step with the economic activities that produce them. The most usual nomenclatures for economic sectors are the Statistical Classification of Economic Activities in the European Community (NACE) and the International Standard Industrial Classification of All Economic Activities (ISIC). The conversion tables for the correspondence are available at the Reference and Management of Nomenclature server in the Eurostat website.

3.3. Decomposition of products

This step is similar to the component of plug-ins of the UMAn model, described in section 2.3, as it has the purpose of disaggregating the products into their material composition, in order to distribute the materials by the economic sectors that sell them. As mentioned earlier, MATCAT and ProdChar are used to divide products according to material categories. It transforms several products into 28 material categories, which is crucial to perform the analysis of mass balances of each material in each economic sector. In this work, however, only 23 material categories were considered. The following changes were made:

 Subcategories FF3 (Lubricants, Oils and Solvents) and FF4 (Plastics and Rubbers) were added to subcategory FF1 (Low ash Fuels);  Subcategory BM7 (Paper and Board) was included in subcategory BM6 (Wood);  Subcategory BM3 (Textile Biomass) was divided between Agricultural Biomass (BM1) and Animal Biomass (BM2), according to the origin of each product being vegetal or animal materials;  The Liquids subcategory (O2) was not considered, since water usage was not included in this study.

3.4. Calculation of the flow of materials

As the input-output tables are in monetary units, as mentioned in section 3., it is necessary to convert monetary flows into material flows using mass content values. Considering the interaction between economic sector i and economic sector j, the monetary flow between both sectors, 푆퐸푖푗, is multiplied by the mass 푚 content of the material m referring to the sales between the same sectors, 푆푃푖푗 , to obtain the physical flow of 푚 material m between them, 푆푀푖푗 , as represented by equation (1).

푚 푚 푆푀푖푗 = 푆푃푖푗 × 푆퐸푖푗 (1)

The same equation is applied to:

푚 푚  Sales from domestic sectors to final consumption: 퐹퐶푀푖푗 , 퐹퐶퐸푖푗, 퐹퐶푃푖푗 ;

푚 푚  Sales from international economic sectors to domestic sectors: 퐼푀푖푗 , 퐼퐸푖푗, 퐼푃푖푗 ;

푚 푚  Sales from international economic sectors to final consumption: 퐼퐹퐶푀푖푗 , 퐼퐹퐶퐸푖푗, 퐼퐹퐶푃푖푗 ;

푚 푚  Sales from domestic sectors to international economic sectors:퐸푀푖푗 , 퐸퐸푖푗, 퐸푃푖푗 .

Therefore, it is necessary to know the mass content for each material in terms of sales between national economic sectors and between national and international economic sectors. However, these variables are unknown and can only be estimated. To do this, a simplification is required by considering that the mass content of the sales from sector i to all other sectors, regardless the type, is equal to the sum of the total mass sold by economic sector divided by the total monetary value of the sales from the sector, as shown in equation (2).

∑ 푆푀푚 + ∑ 퐹퐶푀푚 푚 푚 푚 푗 푖푗 푘 푖푘 (2) ∀푗,푘, 푆푃푖푗 = 푆푃푖 = 퐹퐶푃푖 = ∑푗 푆퐸푖푗 + ∑푘 퐹퐶퐸푖푘

To obtain the mass content of imports and exports, the total mass sales∑ 퐼푀푚and 퐸푀푚, can be used 푗 푖푗 푖 in equation (2). Monetary flows from imports and exports,∑ 퐼퐸푚and퐸퐸푚, are calculated in the same way 푗 푖푗 푖 previously described, using UN Comtrade statistics and the correspondence tables mentioned in section 3.2.

Mass balances are then performed, stating that the sum of materials sold to the domestic economy, the materials exported and the sector consumption is equal to the sum of the materials that enter the sector through domestic extraction, imports or purchases from other sectors. Equation (3) translates the mass balance.

푚 푚 푚 푚 푚 푚 푚 (3) ∑ 푆푀 + ∑ 퐸푀 + ∑ 퐹퐶푀 + 푆퐶푖 = ∑ 푆푀 + ∑ 퐼푀 + 퐷퐸푖 푗 푖푗 푗 푖 푘 푖푘 푘 푘푖 푙 푙푖

Reorganising equation (3) with the economic value taken from IO tables, equation (4) is obtained.

푚 푚 푚 푚 푚 푚 푚 (4) ∑ (푆푃푖 × 푆퐸푖푗 ) + ∑ (퐸푃푖 × 퐸퐸푖 ) + ∑ (푆푃푖 × 퐹퐶푀푖푘 ) + 푆퐶푖 푗 푗 푘

푚 푚 푚 푚 푚 = ∑ (푆푃푘 × 푆퐸푘푖 ) + ∑ (퐼푃푖 × 퐼퐸푙푖 ) + 퐷퐸푖 푘 푙

푚 The only unknowns present in equation (4) are the domestic mass content, 푆푃푖 , and the own 푚 consumption of the sector 푆퐶푖 . These values are obtained through the verification of equation (4), for all 푚 combinations of i and m, as long as all 푆퐶푖 present non-negative values.

With the correspondence tables between products and economic activities, it is possible to conclude that not all materials categories are used in the products sold in each sector. Therefore, the mass content in those cases is zero. To perform this analysis in different time periods or between different countries, changes in prices or variation in products produced must be taken into account.

3.5. Downscaling

This last step has the purpose of scaling down results from the national to the urban perspective, which can be done using a scale factor that reflects the city’s consumption. Such factors can be the share of national workers working in the urban area, %푈푊푖, or the share of gross value added, both per economic sector. The share of population, %푈푃, can also be used to scale down final consumption. Imports and exports balances 푚 푚 between the urban area and the rest of the country, 퐼푀푈푖 and 퐸푀푈푖 may be calculated by performing new mass balances at the urban level. Both of these values are obtained through equation (5), which is the application of equation (3) at the urban scale.

푚 푚 푚 푚 푚 (5) %푈푊푖 × ∑ 푆푀 + %푈푊푖 × ∑ 퐸푀 + %푈푃 × ∑ 퐹퐶푀 + %푈푊푖 × 푆퐶푖 + 퐸푀푈푖 푗 푖푗 푗 푖 푘 푖푘 푚 푚 푚 푚 = %푈푊푖 × ∑ 푆푀 + %푈푊푖 × ∑ 퐼푀 + %푈푊푖 × 퐷퐸푖 + 퐼푀푈푖 푘 푘푖 푙 푙푖

3.6. Limitations

Although the case study presented by Pina et al. (2015) allows to take rough conclusions about the urban areas assessed, this methodology needs to be validated with the analysis of other urban areas, and results shall be interpreted with some attention.

It should be noted that, as Pina et al. (2015) states, the UM model presented in this chapter does not describe the crossing flows in urban areas. Nevertheless, as the materials that cross the urban area are not considered as well, the mass balance is assured. Scaling down has also some limitations, as the use of the share of employees is an assumption that all workers in the country have the same productivity, which is not necessarily correct. Another constraint is the accuracy in registration of workers on NACE codes by the companies, as some companies may work in more than one economic activity and their employees are only listed in their main activity sector.

3.7. Indicators

Outputs resulting from this methodology are the material inputs and the domestic material consumption (DMI and DMC, respectively) of the region per economic sector. The main information that may be determined from these indicators are the internal material consumption, the material intensity, exports intensity and material productivity.

 Material consumption: is assessed by domestic material consumption and material inputs (DMC and DMI), measuring resources consumption by material categories according to MATCAT. Is an indicator of the internal consumption of the economic sectors of the region in study, and the inclusion of DMI accounts for the materials used for the production of export products, which is not accountable in DMC.

 Material intensity: is a proxy of the eco-efficiency of the economy of the region, measured by the DMC to GDP ratio (t/cap/€). The lowest the value of the ratio, the most eco-efficient will be the urban area.

 Material productivity: is given by GDP to DMI ratio, is an indicator of the economic efficiency of the economy.

The work on this thesis relies mainly on material consumption. However, material intensity and productivity are also addressed in comparison between 2000 and 2011, and a relation between the consumption of a specific material category and economic development is established.

4. Case Studies

The urban metabolism model described in chapter3 was applied to 12 European metropolitan regions in order to assess the material consumption and allocation per economic sector. The main purpose is to compare urban areas with different characteristics and estimate the impact that economy has in each one of them. In this chapter, the selection of metropolitan areas is explained, as well as the main sources to collect the necessary data and what type of aggregation was used.

4.1. Selected Metropolitan areas

The methodology was applied to the following metro regions: Berlin, Frankfurt, Hamburg, Paris, Lyon, Lille, Manchester, Liverpool, Lisbon, Porto, Madrid and Stockholm, according to the definition proposed by Eurostat, previously described. Table 5 presents in detail the constitution of each metropolitan region in terms of NUTS 3 regions, population and GDP in 2000 and 2011. The metro areas to analyze were chosen taking into consideration three main factors:

1. Data Availability – The necessary statistics to perform the intended analysis require a certain level of detail, which is not easy to find, especially for metropolitan regions or NUTS3 divisions. However, as some of the metropolitan regions defined by Eurostat are not only a group of NUTS 3 regions but also a NUTS 2, this data collection process was sometimes easier to perform. Examples of this are the urban areas of Manchester, Liverpool, Lisbon, Madrid and Stockholm. Paris is a particular case, as it is a NUTS 2 and also a NUTS 1 ( Table 5).

2. Metropolitan areas as capitals or secondary cities–As far as possible, capital metropolitan areas were chosen in order to study the main differences between the countries and their main cities. Considering the 6 countries addressed, Germany, France, UK, Portugal, Spain and Sweden, only London was not possible to assess due to lack of detailed data.Secondary urban areas within the countries discriminated previously were also selected, in order to compare with the capitals and address the variety of economies from city to city, at the national level.

3. Population and GDP –Another factor considered on choosing the metropolitan areas was the population and economic development of each one of them. The goal was to select urban regions with different characteristics, in order to compare the main differences among them. In absolute values, Porto has the lowest population and also lowest GDP, whereas Paris has the highest population and Stockholm the highest GDP. In terms of percentage of the national level, Liverpool only accounts for around 2.4%of the UK’s population while Lisbon represents the highest concentration with a share of around 26%., as can be consulted seen in Table 5.

Table 5- Composition of metro areas and population.

Population GDP [€/cap]

Metro Region NUTS 2013 Country Country 2000 2011 2000 2011 % % 1 NUTS 3: Nord Lille 2 554 638 4,2% 2 579 208 4,0% 20500 27800

1 NUTS 3: Rhône Lyon 1 588 910 2,6% 1 744 236 2,7% 27700 37400

8 NUTS 3: Essonne, Hauts-de- Seine, Paris, Seine-Saint-Denis, Seine-et-Marne, Val-d'Oise, Val- Paris 11 019 991 18,1% 11 852 851 18,2% 38100 51800 de-Marne, Yvelines Also a NUTS 2 and NUTS 1: île de France 10 NUTS 3: Barnim, Berlin, Dahme-Spreewald, Havelland, Märkisch-Oderland, Oberhavel, Berlin 4 922 511 6,0% 5 055 116 6,3% 22 750 29245 Oder-Spree, Potsdam (Kreisfreie Stadt), Potsdam-Mittelmark, Teltow-Fläming 8 NUTS 3: Frankfurt am Main (Kreisfreie Stadt), Groß-Gerau, Hochtaunuskreis, Main-Kinzig-

Frankfurt Kreis, Main-Taunus-Kreis, 2 481 064 3,0% 2 553 119 3,2% 42009 49844 Offenbach am Main (Kreisfreie Stadt), Offenbach (Landkreis), Wetteraukreis 7 NUTS 3: Hamburg, Harburg,

Hamburg Herzogtum Lauenburg, Pinneberg, 3 056 199 3,8% 3 209 759 4,0% 21047 25897 Segeberg, Stade, Stormarn 1 NUTS 3, also a NUTS 2: Área Lisbon 2 624 511 25,5% 2 822 761 26,7% 17962 23389 Metropolitana de Lisboa 1 NUTS 3: Área Metropolitana do

Porto Porto 1 248 031 12,1% 1 288 468 12,2% 12156 15457

1 NUTS 3, also a NUTS 2: Madrid Madrid 5 185 931 12,8% 6 394 252 13,7% 21300 31000

4 NUTS 3: East Merseyside,

Liverpool Liverpool, Sefton, Wirral 1 370 924 2,2% 1 503 567 2,4% 21100 22800 Also a NUTS 2: Merseyside 5 NUTS 3: Manchester, Greater Manchester. SW, Greater Manchester. SE, Greater

Manchester Manchester. NW, Greater 2 515 914 4,0% 2 673 791 4,2% 24300 24400 Manchester NE Also a NUTS 2: Greater Manchester 1 NUTS 3, also a NUTS 2: Stockholm 1 803 377 20,3% 2 054 343 21,7% 44600 60000 Stockholm

4.2. Data collected

For this work, it was necessary to collect input-output tables representing the monetary trades between economic sectors, population, number of employees by economic sector and gross domestic product, to assess the resource productivity. All these data were collected for the years 2000 and 2011, at the regional and national level. Regional data about employment was found to be the most difficult to find due to the fact that some national statistics offices do not have data available with the level of disaggregation required. The sources used to collect the necessary data are listed below in Table 6.

Table 6 - Data description and sources.

Data Description Source Domestic and import tables for each Input-output tables World Input-Output Database (WIOD) country Countries, per NUTS divisions and MA Eurostat MA of Germany Destatis (Federal Statistical Office of Germany) MA of Portugal INE (Instituto Nacional de Estatística) Neighbourhood Statistics (Office for National Number of workers per economic Manchester MA Statistics of Government of UK) activity Insee (Institut national de la statistique et des MA of France études économiques) No. of workers in Coke and refine sector in Refinery of Matosinhos databook, 2011 2011 National level and for some metropolitan Eurostat regions Population ESPON (European Observation for Territorial Per NUTS divisions development and Cohesion). Gross Domestic Product (GDP) All MA, except Porto Eurostat & Porto INE (Instituto Nacional de Estatística) Gross Value Added (GVA)

For a few exceptions, where data was not available, the estimation of the share of workers was based in other years available (as 2001 or 2002), and assessing if this ratio was fairly constant over the years. An example of this situation is the data for Coke, Refined Petroleum and Nuclear Fuel of Porto in 2000 and Stockholm in 2011.

4.3. Economic sector aggregation

The calculation of material flows using the previously described method was performed using a level of economic aggregation of 35 sectors, according to NACE Rev.2, discriminating the different types of manufacturing and the several service sectors, as shown in Table 7. However, for some urban areas, the statistics concerning the number of workers were not available with the required level of disaggregation. This means that instead of having the number of workers for 35 sectors, only more aggregate data was available. As

such, in those cases, the same share of workers was considered for the sectors for which only aggregate data was available. For example, if only aggregated data was available for a sector A that combined sectors S01 and S02 presented in the table below, the share of workers of sector A was assumed for the share of workers of sectors S01 and S02.Nonetheless, the expected impact of this assumption is small as it was only applied in some cases within the service sectors, which don’t generate as much material flows as industry and manufacturing. To validate this, an analysis was performed by estimating the material flows of Paris and Lisbon using the same method by considering only a level of aggregation of 27 and 25 sectors, respectively.

Table 7 - Economic sectors disaggregation

NACE Rev. 1.1. NACE Rev. 2 Sector Description correspondence correspondence S01 Agriculture, Hunting, Forestry and Fishing 01-05 01-03 S02 Mining and Quarrying 10-14 05-09 S03 Food, Beverages and Tobacco 15-16 10-02 S04 Textiles and Textile Products 17-18 13-14 S05 Leather, Leather and Footwear 19 15 S06 Wood and Products of Wood and Cork 20 16 S07 Pulp, Paper, Paper , Printing and Publishing 21-22 17-18 S08 Coke, Refined Petroleum and Nuclear Fuel 23 19 S09 Chemicals and Chemical Products 24 20-21 S10 Rubber and Plastics 25 22 S11 Other Non-Metallic Mineral 26 23 S12 Basic Metals and Fabricated Metal 27-28 24-25 S13 Machinery, Nec 29 28 S14 Electrical and Optical Equipment 30-33 26-27 S15 Transport Equipment 34-35 29-30 S16 Manufacturing, Nec; Recycling 36-37 31-33 S17 Electricity, Gas and Water Supply 40-41 35-39 S18 Construction 45 41-43 S19 Sale, Maintenance and Repair of Motor Vehicles and Motorcycles; Retail Sale of Fuel 50 45 S20 Wholesale Trade and Commission Trade, Except of Motor Vehicles and Motorcycles 51 46 S21 Retail Trade, Except of Motor Vehicles and Motorcycles; Repair of Household Goods 52 47 S22 Hotels and Restaurants 55 55-56 S23 Inland Transport 60 49 S24 Water Transport 61 50 S25 Air Transport 62 51 S26 Other Supporting and Auxiliary Transport Activities; Activities of Travel Agencies 63 52-53 S27 Post and Telecommunications 64 58-63 S28 Financial Intermediation 65-67 64-66 S29 Real Estate Activities 70 68 S30 Renting of M&Eq and Other Business Activities 71-74 69-82 S31 Public Admin and Defence; Compulsory Social Security 75 84 S32 Education 80 85 S33 Health and Social Work 85 86-88 S34 Other Community, Social and Personal Services 90-93 90-96 S35 Private Households with Employed Persons 95-97 97-98

Overall, the information available was as follows. Lisbon and Porto had a disaggregation of 35 sectors, whereas the German urban areas had 21 sectors, the French 27, Madrid, Liverpool and Manchester 32 and

Stockholm was analyzed with an aggregation of 32 and 29 economic sectors, for 2000 and 2011, respectively (see Appendix B).

4.4. Metropolitan areas economic context

The importance of these metropolitan regions in their respective countries is very varied. To assess the share of national income generation, gross value added (GVA) is used instead of gross domestic product (GDP), due to the availability of data for different economic sectors. The main difference between both these variables is that whereas GVA represents the monetary value of products and goods of an certain area, GDP measures the final market value of those same items.

As Germany has 68 metropolitan areas, its gross value added is dispersed, and so Berlin, Hamburg and Frankfurt represent only 5% each of the national GVA, in spite of Berlin being the capital. The same situation is verified for Lyon, Lille, Stockholm, Manchester and Liverpool. However, in the cases of Paris and Lisbon, they represent around 30% and 37% of the national GVA, respectively, having a great influence in the country’s capital income. Madrid and Porto contribute to the GVA of the irrespective country with around 18%.

Figure 6 represents the distribution of GVA per economic activity (agriculture, industry and services) for each metropolitan area, in 2000 and 2011. The most productive economic activity is the tertiary sector, which generates between 71% and 86% of the total GVA of all metropolitan areas, unlike agriculture that has an insignificant share of around 1%. Overall, all metropolitan areas show a transition towards a more services economy, with the main reductions on the weight of the industry sector being observed for Porto (8 per cent points), Madrid (9 per cent points), Manchester (8 per cent points) and Lille (6 per cent points). In spite of the tendency for the tertiary sector to overcome the others in all metropolitan areas, Porto and Liverpool are still very industrialized areas, with a current share of GVA of 25% and 22% coming from the industry sector.

Figure 6 - Distribution of GVA per economic sector.

5. Results

This chapter is structured as follows. First, validations of the methodology at a national scale by comparison with data available in Eurostat and of the assumptions made regarding the level of aggregation of economic activities are presented. Then, total results of DMC and DMI for all urban areas are shown and compared to previous studies. This is followed by a contextualization of the metropolitan areas within their countries, to compare urban and national material use. Afterwards, the consumption of materials of the European metropolitan areas considered is compared for the year of 2011. Finally, the evolution of all metropolitan areas between 2000 and 2011 is presented and discussed. Results are presented in detail in Appendix B.

5.1. Methodological validations

In order to validate the methodology, the results obtained for domestic material consumption (DMC) at the national level are compared with data available in Eurostat in Table 8. Comparing the values obtained for both years with the available data, it is possible to conclude that the results obtained present differences between 2% for Spain and 8% for Sweden in 2000, and 1% for Spain and 9% for UK in 2011.

Table 8 - Results at National Level and comparison with Eurostat.

DMC [t/cap] Results obtained Presented in Eurostat

Country 2000 2011 2000 2011 France 14.91 13.42 14.55 12.38 Germany 18.53 17.73 17.70 17.05 Portugal 18.74 18.27 19.48 17.26 Spain 17.29 11.25 17.01 11.15 Sweden 21.74 23.81 20.18 22.29 United Kingdom 12.21 9.99 12.54 9.20

In order to assess how the assumptions made to disaggregate data concerning the number of workers influenced the results, a validation was performed. The validation consisted in applying the methodology to a metropolitan area with a disaggregation of 35 sectors, then it was applied to the same region with a different aggregation. After this, results were compared in order to assess variations (see Appendix C).

This analysis was applied to Paris in 2000, and Lisbon and Porto in 2011, due to the fact that they were the only metro areas that had the required data available to do the analysis with a disaggregation of 35 sectors. Deviations of around 0.4% for Paris, and 3.8% for Lisbon and Porto were obtained regarding DMC. Differences

on consumption by economic activities were significant for the biomass products and construction products sectors in Paris, as the error was of about 139.7% and 21.9%, respectively. However, this is an acceptable error these sectors only represent 1% and 2%, respectively, of the DMC of this metropolitan region. For Lisbon and Porto, deviations of 16.4% and 19.1% were found for the services sector, respectively. Differences in Exports varied between 3.9% for Lisbon and 9.0% for Paris. In terms of material consumption, the differences varied between 0.3% and 8.2%, with the main differences being present in the fossil fuels category, which varied between 1.4% in Paris and 8.2% in Lisbon, followed by non-metallic minerals, whose errors stand between 1.6% for Paris and 3.6% for Lisbon. As a consequence of this analysis, the following results are presented considering that there is an uncertainty associated to their accuracy.

5.2. Total DMC and DMI

The application of this method resulted in values of total DMC per capita between 8.30 and 22.96 tons, and total DMI per capita from 9.23 to 32.24 tons, as presented in Table 9. Some of the results can be compared to other studies performed on the same metropolitan area.

Considering the year 2000, some details are discussed for a few urban areas. Results for Paris are close to the ones obtained by Pina et al. (2015), with differences between 4.8 and 5.0%, which was expected, due to the fact that the methodology used in both cases is the same. The reason why results are not exactly the same is due to the fact that in Pina et al. (2015) is used IO tables from OECD, whereas in this thesis is utilized WIOD tables. Comparing the results with the study presented by Barles (2009), Paris results show a discrepancy of around 50% for the DMC value is notorious in spite of the years of study being close (2000 and 2003). Besides the different methods applied, Barles assumes wastes as exports, as mentioned previously, which may explain the gap of around 50% in DMC. However, while the values of DMI are more similar, they still present a large difference.

For the metro area of Hamburg, the disparity between DMI of each study is explained by the account of the flows that enter and exit the harbor existent in it. While the cited study takes into account these flows, the methodology applied does not use such detailed information and therefore only tries to account for the materials that are processed in each urban area and not just passing by. However, DMC also presents divergent values, possibly due to the fact that only the consumption of 3 categories of materials were accounted for by Hammer & Giljum, which may have resulted in a poor assessment of the material flows and, therefore, of the consumption.

Regarding the results obtained for Lisbon, it is noteworthy that the results obtained are similar than those obtained in the analysis performed by Pina et al., where variations are justified with the differences in IO tables, as mentioned previously. Values presented are also close to those obtained in the analysis performed

by Niza et al., 2009. The results presented in Rosado et al. (2013) are very different, with a DMC of Lisbon for the year 2005 of 10.8 tons per capita.

Table 9 - Results obtained and comparison with other studies.

DMC DMI Other studies [t/cap] [t/cap] Metro Area Source DMC DMI 2000 2011 2000 2011 Year [t/cap] [t/cap] Lille 13.86 12.70 16.51 15.65 -

Lyon 17.63 15.51 22.64 20.96 -

7.1 12.3 2003 Barles (2009) Paris 14.72 11.85 16.75 13.14 15.5 17.6 2000 Pina et al. (2015)

Berlin 20.26 17.86 21.14 18.94 -

Frankfurt 19.37 17.93 21.78 19.44 - Hammer & Giljum Hamburg 22.86 20.90 31.11 27.19 11 65 2000 (2006) Niza et al. (2009) (City 20.1 20.4 2004 of Lisbon, not MA) Lisbon 16.46 18.97 17.94 20.37 10.4 - 2005 Rosado et al. (2013) 17.1 18.9 2000 Pina et al. (2015)

Porto 22.96 16.45 32.24 19.92 -

Madrid 18.00 12.91 20.19 15.61 5.9 - 2010 Sastre et al. (2015)

Liverpool 8.30 8.32 10.24 9.23 -

Manchester 10.54 9.05 13.62 10.44 - 10.34 12.12 2011 Rosado et al. (2016) Stockholm 19.19 22.02 22.39 25.72 Kalmykova et al. 10.1 16.3 2011 (2015)

Regarding 2011, a few notes can be taken for the metropolitan areas of Madrid and Stockholm. In the case of Madrid, Sastre et al. (2015) assessed a DMC of 5.9 tons per capita for 2010, whereas the present study estimated a DMC of 12.91 tons per capita in 2011. As previously mentioned, this may be due to the fact that the cited study did not account for railway flows.

For the Stockholm metro region, results are very distinguished from previous studies published by Kalmykova et al. (2015) and Rosado et al. (2016). Although they use different approaches, the main differences were found to be in the metallic minerals and biomass flows. As shown in the next section, these values were found to be quite high, whereas both previous studies have a value around 10 times lower due to the fact that they do not consider slag of mineral ores as metallic minerals inputs, but as waste, and their assumptions for wood extraction in Sweden being quite lower than the value reported in Eurostat.

Another important aspect to mention are the results of Liverpool and Manchester, which present really low values comparing to other metropolitan areas. However, these values are in conformity with the

values of DMC and DMI of the United Kingdom (UK), which also has low DMC of 9.20 tons per capita, as will be discussed in the next section.

5.3. Metropolitan areas in a national context

In this section, a comparison between the results obtained at a national and metropolitan area scales for the year 2011 is performed.

In France, only Lyon metropolitan area, with 15.51 tons per capita, has a higher material consumption than the country as a whole (13.42 tons per capita),whereas Paris has the lowest value with a DMC of 11.85 tons per capita, as can be seen in Figure 7. Assessing the consumption per material category, all metropolitan areas and the country have similar consumption in every categories, except for non-metallic minerals, which is around 45% higher in Lyon than in the rest of the national cases, as shown in Figure 7. In terms of economic activities, the allocation of materials is also similar in almost every sectors, apart from the chemicals and fuel products sector and exports, whose variations are between 0.21 tons per capita Paris and 0.44 tons per capita in Lyon for the first one, and between 1.28 tons per capita in Paris and 5.45 tons per capita in Lyon for the second one.

Figure 7 - DMC and DMI of France and correspondent Metropolitan Areas.

Regarding German, that has a DMC of 17.73 tons per capita, the capital Berlin and Frankfurt present a similar consumption, with 17.86 and 17.93 tons per capita, as shown in

Figure 8. However, Hamburg diverges from these values, as it consumes around 20.9 tons per capita, which is about 17% higher than the previous values. In terms of material categories of consumption, the results are very similar, especially between Germany, Berlin and Frankfurt, as visible in

Figure 8. The main difference from Hamburg to the other regions is due to the consumption of non- metallic minerals, which is between 17% and 43% higher than in Germany and Berlin, respectively. Relatively to economic activities, the status is not as similar as the French framework. While Berlin and Frankfurt are very similar metro regions in terms of material consumption by economic activity, with very low consumptions for agricultural and industrial sectors, Hamburg presents higher consumptions for exports and for the chemical and fuel products sector. The results for Germany as a whole demonstrate its strong industrial sectors, with significant material consumptions for the construction, metallic products, and machinery and equipment sectors. Agriculture and mining have also relevant input values, with 1.80 tons per capita, whereas its values in all metropolitan areas are only between 0.22 and 0.83 tons per capita.

Figure 8 - DMC and DMI of Germany and correspondent Metropolitan areas.

In what concerns Portugal, shown in Figure 9with a national DMC of 18.27 tons per capita, Lisbon presents a much more similar profile in terms of DMC by material category, presenting 18.97 tons per capita, while Porto consumes only 16.45 tons per capita with a lower consumption in non-metallic minerals by about 13%. In terms of economic activities, gross fixed capital formation (GFCF) was found to be very relevant for both the country and the metropolitan areas. Differences are highlighted in the services sector, which is 50% higher in Lisbon when compared to Porto and Portugal, and exports, which are around 60% higher in Porto when compared to Lisbon and Portugal. Another important aspect to mention is that the agriculture and mining sector has a national input of 1.03 tons per capita, which is 62% and 76% higher than in Lisbon and Porto, respectively.

Figure 9 - DMC and DMI of Portugal and correspondent Metropolitan areas.

In Spain, with a DMC of 11.25 tons per capita, the evaluation is performed with the capital, Madrid, which has a DMC of 12.91 tons per capita, as seen in Figure 10. Material consumption is balanced and very similar with some differences in metallic minerals, fossil fuels and chemicals and fertilizers, which are 55%, 37% and 27% less consumed for Spain as a whole, respectively. Spain only has a higher consumption than Madrid in biomass, of 8%. The overview of economic activities inputs is more heterogeneous, mainly due to the services and machinery and equipment sectors that are 138% and 86% higher in Madrid. Nonetheless, the biomass products and agriculture and mining sectors have 62% and 79% more inputs for Spain.

Figure 10 - DMC and DMI of Spain and correspondent Metropolitan area.

For Sweden and Stockholm, the panorama is quite similar, with 23.81 and 22.02 tons per capita of DMC, respectively, as shown in Figure 11. Considering the different material types, it can be observed that the consumption of biomass is higher in Stockholm while non-metallic minerals are higher in Sweden. In terms of DMI, Sweden presents a significantly superior value than Stockholm, mainly due to exports that are 146.8% higher. Other sectors that might assist in the explanation of the main differences present in Figure 11 are agriculture and mining, metallic products and biomass products, which have 454%, 387% and 110% more inputs for Sweden than for Stockholm, respectively.

Figure 11 - DMC and DMI for Sweden and correspondent Metropolitan area.

The United Kingdom is compared with two of its most relevant metropolitan areas, Liverpool and Manchester in Figure 12. The DMC of the country was found to be higher than those of the metropolitans areas with 9.99 tons per capita while Liverpool and Manchester have 8.32 and 9.05 tons per capita, respectively. The consumption by material type is quite similar for almost all categories for both the country and metropolitan areas, except for non-metallic minerals, which is 51% and 40% higher in the UK than in Liverpool and Manchester, respectively. In what concerns DMI by economic activity, the metropolitan areas are more similar between them than with the national level. The main differences between Liverpool and Manchester are in utilities and exports, where Liverpool presents results 107% and 50% lower than Manchester, respectively. Comparing with the UK, differences are reasonably perceptible in Figure 12. Inputs for utilities are practically null in the UK and inputs for services are 1.19 tons per capita, which is around 46% lower than for Liverpool and 51% lower than for Manchester. However, there is an evident increase in the machinery and equipment and the construction sectors, as well as exports, as results are 0.50 and 2.58 tons per capita, which corresponds to 99%, 75% and 47% higher than to the ones obtained for Manchester, and 99%, 81% and 65% than for Liverpool, respectively.

Figure 12 - DMC and DMI of UK and correspondent Metropolitan areas.

5.4. Comparison of EU Metropolitan areas in 2011

Domestic material consumption measures resource consumption of materials, the maintenance of production and the consumption of different economic activities. This indicator is represented in Figure 13, disaggregated by material type, and Figure 14, disaggregated by economic activity, and some aspects can be highlighted.

Addressing initially the results shown in Figure 13, the metropolitan areas with the highest and lowest consumption are Stockholm and Liverpool, with 22.02 and 8.32 tons per capita, respectively. Non-metallic minerals are found to dominate resource consumption, being responsible for between 19% and 65% of DMC. The other two most relevant material types are biomass, responsible for between 18% and 38%, and fossil fuels, between 10% and 38%.The German metropolitan areas register the highest consumption of fossil fuels, whereas the Portuguese and French regions are the lowest consumers of this resource. The use of biomass is also found to have risen for German regions.

Figure 13- DMC per Material Category for Metropolitan areas in 2011.

Figure 14 presents the results obtained for DMI disaggregated by economic activity in 2011 for all metropolitan areas considered. The services sector was found to be the most relevant sector in all metropolitan areas, with a material input between 1.54 tons per capita for Lille and 6.64 tons per capita for Stockholm, followed by gross fixed capital formation, between 0.27 tons per capita for Liverpool and 7.06 tons per capita for Lisbon and final consumption, between 2.84 tons per capita for Madrid and 6.60 tons per capita for all the German metropolitan areas. Nonetheless, the results also show the diversity of the urban areas in terms of their industrial activities. For example, Madrid presents a relevant machinery and equipment sector consumption, with 3.78 tons per capita, while Hamburg and Lyon have significant consumption in the chemicals and fuel products industry, with 2.66 and 3.71 tons per capita, respectively. The importance of exports was found to vary significantly across the considered metropolitan areas, with Lyon (26%) and Hamburg (23%) exporting more of their DMI and Berlin (6%) and Lisbon (7%) being the ones that least export.

Figure 14 – DMI per economic sector for Metropolitan areas in 2011.

Another very interesting study to address is the consumption of the different types of materials by the several economic sectors, in order to understand the main correspondences in terms of materials input and consumption. Circos is an online software package that organizes data in a circular layout, being ideal to rearrange all the information and define the allocation of different types of materials in the different economic sectors. Some sectors are aggregated, in order to analyze more accurately the information provided by the circular graph.

Observing Figure 15, it is notorious that Berlin and Frankfurt have an identical materials distribution per economic activity. Non-metallic minerals are distributed with 30% to services and 30% to final consumption, and almost 20% to utilities, with the other 20% being allocated into diverse manufacture sectors. Fossil fuels are mainly destined to final consumption and services, with 30% and 40%, respectively. Another material with high consumption levels, biomass, is also mainly allocated to the services sector and final consumption, with 33% and 45%, respectively. Exports are not so prominent in these metropolitan areas, in opposition to final consumption and services, which have the highest material input. For the metropolitan area of Hamburg, shown in Figure 16a, the overview is quite different. Although the major material categories regarding consumption are also non-metallic minerals, biomass and fossil fuels, the distribution by economic

activities is more heterogeneous. NM provides 22% to Coke, Refined Petroleum and Nuclear Fuel, constituting about 90% of its inputs, and also represents almost 100% to utilities, 50% to construction and 70% to agriculture and mining. Its resources are also distributed by services and final consumption, with 12% and 20%, whereas FF provide 22% and 19%, respectively. 42% of fossil fuels are also exported. Biomass is mainly allocated to final consumption, services and exports, with 40%, 32% and 10%, respectively.

For Lille, shown in Figure 16b, non-metallic minerals and biomass represent the groups with highest consumption, and are more distributed through the economy. 20% of NM go to gross fixed capital formation and also 20% to coke and refined petroleum sector. Other shares between 1% and 10% are allocated to final consumption, exports, machinery and equipment, basic metals, non-metallic products and chemical products industry. BM has a flow of 60% directed to final consumption, and smaller values to the services (10%) and the food and beverages (7%) sectors. Fossil fuels provide 36% to exports, 34% to final consumption, 14% to services and 4% to basic and fabricated metal products. For the other French metropolitan areas, Lyon and Paris, the overview is identical. As shown in Figure 17, NM has a diverse economic sector distribution, supplying coke and refined petroleum with 24%, chemical industry with 16%, gross fixed capital formation with 8% and all the other manufacture sectors with smaller shares. Fossil fuels, which have a higher consumption value for Paris than for Lyon, provide inputs for services, final consumption and exports. Biomass has similar consumptions as fossil fuels, with a slightly higher percentage to final consumption in Paris, of 62%, than in Lyon, with 58%.

Lisbon and Porto, represented in Figure 18, have a very similar allocation of materials in the economy. NM dominates the consumption in both metropolitan areas, as it provides 50% of its total consumption to gross fixed capital formation, between 12% and 21% to services, 6% to final consumption, 2% to exports and the rest for almost every manufacture sectors. Biomass has also a quite identical distribution, with 42% to final consumption, 14% to exports and 10% to services. MM, CF and O also present a very similar distribution, whereas FF have some differences, as Porto has a higher share than Lisbon. The main consumption is made by the services sector, final consumption and exports. Nevertheless, Porto exports 50% of its FF input, whereas Lisbon only exports 30%.

In addition to having very similar values of DMC per capita, as mentioned previously, Liverpool and Manchester have an allocation of materials into sectors practically equivalent, as shown by Figure 19. Contrary to the material consumption of other metropolitan areas, the UK metro regions have a higher consumption of fossil fuels and biomass than non-metallic minerals. FF is naturally distributed to exports, services and final consumption, with similar percentages of the other metropolitan areas of 12%, 32% and 48%, respectively. BM provides about 68% of its inputs to final consumption, allocating 20% to services and the rest to exports and industries.

Madrid, presented in Figure 20a, shows major flows of fossil fuels, non-metallic minerals and biomass, Fossil fuels sustain the services sector with 30%, final consumption with 20% and exports with 32%. Non-

metallic minerals are mainly consumed for utilities, contributing with 34%, construction, services and gross fixed capital formation with 12% and chemical products with 8%. Biomass is divided by the same sectors as FF, contributing to final consumption with 54% of the resources, 28 % to services and 10% to exports.

Stockholm, presented in Figure 20b, has a higher consumption of biomass, non-metallic minerals and metallic minerals. It has a more balanced input from each material type, mainly non-metallic minerals, metallic minerals, biomass and fossil fuels. Distribution is varied, and inputs for the main activities come from these for materials. For example, services are provided with 28% of NM, 26% of BM, 22% of MM and 8% of FF, and final consumption with 30% of BM, 24% of FF, 12% of MM and 10% of NM. These 4 materials also share inputs for gross fixed capital formation, which has a significant share of economic activities consumption. NM is the main resource used, providing 28% of the total DMI.

Figure 15 - Berlin (a) and Frankfurt (b) Material Flows in 2011.

Figure 16 - Hamburg (a) and Lille (b) Material Flows in 2011.

Figure 17 - Lyon (a) and Paris (b) Material Flows in 2011.

Figure 18 - Lisbon (a) and Porto (b) Material Flows in 2011.

Figure 19 - Liverpool (a) and Manchester (b) Material Flows in 2011.

Figure 20 - Madrid (a) and Stockholm (b) Material Flows in 2011.

5.5. Evolution of Metropolitan areas from 2000 to 2011

Figure 21 and Figure 22 shows a comparison of the DMC and DMI of the 12 metropolitan areas for the year 2000 and 2011, respectively. In general, a light decrease of consumption is visible between 2000 and 2011, except for Lisbon and Stockholm where increases of 2.51 and 2.83 tons per capita, respectively, can be observed. Regarding the discrepancy of this last metropolitan region with other studies, it is noteworthy the significantly increase of metallic minerals from 2000 to 2011. Stockholm also registers the highest consumption of biomass in both periods. In opposition, Madrid and Porto suffer an accentuated decrease of 5.09 and 6.51 tons per capita, respectively. It is also interesting to observe that, in 2000, Porto and Hamburg have the maximum consumption, of these twelve metro regions, while in 2011 Stockholm and Hamburg stand out.

The decrease in DMI from 2000 to 2011 can easily be attributed to a decrease in consumption of primary and secondary sectors, which varied between 19.9% and 48.6% in 2000 and were reduced to between 18.3% and 49.6% in 2011. Conversely, the services sector dominates material consumption, in general, increasing from between 51.4% and 80.1% in 2000 to between 50.4% and 85.4% in 2011. Maximum value for primary and secondary sectors in 2011 is higher than in 2000 and minimum consumption share is lower in 2011 due to Madrid having the opposite evolution. It is also relevant to consider the importance of gross fixed capital formation (GFCF) in some urban areas, such as Porto, Lisbon and Stockholm. In the case of the Portuguese urban areas, GFCF is the highest consumer of materials both in 2000 and 2011.

Figure 21 - DMC of Metropolitan Areas for 2000 and 2011.

Overall, exports were found to be diminishing, especially for metro regions as Porto and Hamburg, where they were very relevant. This is easily understood due to the previously observed decrease in material consumption by the industrial sectors. However, it is also important to notice that not all urban areas have decreased their exports, with Lyon, Madrid and Stockholm having increase between 2000 and 2011.

Figure 22 - DMI for Metropolitan Areas in 2000 and 2011.

The evolution of the material intensity of the twelve MAs (DMI vs. GDP) between 2000 and 2011is represented in Figure 23. A higher value represents an urban area with a higher efficiency.

The German, French and Spanish metropolitan areas were found to improve their overall efficiency through a very significant growth in economic output and modest decrease of material consumption. Liverpool has almost the same consumption but with a slightly higher economic output, whereas Manchester managed to slightly reduce the consumption and maintain practically the same income generated. Porto was able to achieve a very significant reduction of consumption while modestly increasing its GDP, while Lisbon and Stockholm obtained a growth in GDP but at the expense of increasing their material consumption.

Figure 23 - Evolution of Material Intensity for Metropolitan Areas between 2000 and 2011.

In terms of material productivity, which is given by the GDP to DMI ratio, the values were found to vary between 377.02 €/t/cap and 2311.21 €/t/cap in 2000, and increased to values between 776.11 €/t/cap and 3943.27 €/t/cap in 2011. The highest and lowest productivity values mentioned previously belong to Paris and Porto, respectively. The most relevant growth occurred for Porto, whereas the least significant increase took place for Stockholm with 18%, going from 1974.24 €/t/cap to 2332.42 €/t/cap.

Figure 24 to Figure 29 attempt to establish a relation between the input of a specific material category and economic development, measured through GDP per capita. To do this, all results obtained for the metropolitan areas, irrespectively of the year, were considered.

For fossil fuels, as GDP increases, the tendency is to decrease inputs, as represents Figure 24, as well with non-metallic minerals, observed from Figure 28. Relatively to biomass, in Figure 25, there is a clear propensity to increase the inputs of this resource with economic growth, which happens as well with metallic minerals, shown in Figure 27, chemicals and fertilizers, in Figure 26, and other materials, in Figure 29.

These results suggest that when cities are in earlier stages of development, they require the consumption of non-metallic minerals to develop infrastructure. However, as they grow, they begin to require other types of materials to support new economic activities and fulfill the needs of a wealthier population.

Figure 24 - Fossil Fuels inputs per GDP.

Figure 25 - Biomass inputs per GDP.

Figure 26 – Chemical and Fertilizers inputs per GDP.

Figure 27- Metallic Minerals inputs per GDP.

Figure 28 - Non-metallic minerals inputs per GDP.

Figure 29 - Other materials inputs per GDP.

The same analysis was performed for the several economic sectors, with the results being shown in Figure 30 to Figure 41. For the agriculture and mining sector, shown in Figure 30, with exception for few cases, a tendency to decrease is seen with the increase of GDP, as well as for biomass products industry, in Figure 31. Chemical and fuels products, construction products industries and metallic products shown in Figure 32, Figure 33 and Figure 34 respectively, also decrease to some extent with the economic growth of the metropolitan area, whereas machinery and equipment industries, in Figure 35, are quite constant with the variation of GDP. The inputs for utilities and gross fixed capital formation tends to raise when GDP increases, although the last sector mentioned has a less accentuated increase than the other two, as is visible in Figure 36 and Figure 40, respectively. Looking at Figure 37, the construction sector is found to have a light tendency to decrease its inputs, with exception for the two highest values of GDP. It should be noted that this does not contradict what was said before in terms of the development of infrastructures, as significant decreases can be observed in the construction products sector. For services sector in Figure 38 conclusions can’t be taken, as results are too dispersed.

According to results for final consumption presented in Figure 39, there seems to be a slight increase, followed by a stabilization of inputs. Analyzing the final graph, from Figure 41, exports have a clear tendency to diminish with the increase of GDP, which reflects a tendency of metropolitan areas to become increasingly services oriented economies.

Figure 30–Agriculture and mining sector inputs per GDP.

Figure 31 – Biomass products industry inputs per GDP.

Figure 32 - Chemicals and fuels products inputs per GDP.

Figure 33 - Construction products industry inputs per GDP.

Figure 34 - Metallic products industry inputs per GDP

Figure 35 - Machinery and Equipment industry inputs per GDP.

Figure 36 - Utilities inputs per GDP.

Figure 37 - Construction sector inputs per GDP

Figure 38– Services sector inputs per GDP.

Figure 39 - Final consumption per GDP.

Figure 40 - GFCF inputs per GDP.

Figure 41 - Exports inputs per GDP.

6. Conclusions and Future Work

6.1. Conclusions

The goal of this work was to calculate the total material consumption of 12 metropolitan areas in Europe for 2011, and their evolution since 2000, using publicly available statistical data and applying a methodology that evaluates consumption at the national level, and then uses a scaling factor to scale down the results for regional level. A disaggregation of 35 economic sectors was defined, in spite of not all the data being available with this disaggregation. The analysis focused on 6 different material categories: fossil fuels, metallic minerals, non-metallic minerals, biomass, chemicals and fertilizers and others.

The application of this method resulted in values of total DMC per capita between 8.30 and 22.96 tons in 2000, for Liverpool and Porto, respectively, and between 8.32 and 22.02 tons per capita in 2011, for Liverpool and Stockholm. For 2011, non-metallic minerals were found to dominate resource consumption, being responsible for between 19% and 65% of DMC. The other two most relevant material types are biomass, responsible for between 18% and 38%, and fossil fuels, between 10% and 38%. The German metropolitan areas register the highest consumption of fossil fuels, whereas the Portuguese and French regions are the lowest consumers of this resource. The use of biomass is also found to have risen for German regions. Regarding exports, the metropolitan regions with the highest and lowest shares of exports in DMI where Lyon, with 26%, and Berlin, with 6%. Lisbon was also found to be one of the lowest regions in terms of exports, with 7%, followed by Frankfurt, with 8%. The economic sectors with highest material consumption are services, with exports and final consumption, for most of the cases, accounting also for significant shares of DMI. The main materials consumed are fossil fuels, non-metallic minerals and biomass.

Regarding material productivity, given by the GDP to DMC ratio, it varied between 377.02 €/t/cap and 2311.21 €/t/cap in 2000, and increased to values between 776.11 €/t/cap and 3943.27 €/t/cap in 2011. The highest and lowest productivity values mentioned previously belong to Paris and Porto. The most relevant growth occurred for Porto, whereas the least significant increase took place for Stockholm with 18%, going from 1974.24 €/t/cap to 2332.42 €/t/cap.

Relatively to the evolution of material inputs, biomass, metallic minerals and non-metallic minerals have the tendency to increase with economic development, measured through GDP per capita, whereas non- metallic minerals and fossil fuels tend to decrease. Considering economic activities, the relevance of utilities, services and gross fixed capital formation consumption tends to increase with economic growth, in opposition of exports and the construction products sector.

6.2. Future work

While this work constitutes a relevant step for the application of urban metabolism methodologies to several case studies, several improvements can be performed. One of the main issues concerns the methodology that was applied, which requires a more robust analysis on the impacts of the assumptions considered. This would provide a better understanding of the limitations of this methodology. For example, the use of number of workers as a proxy for the downscaling of the results should be investigated and compared with other potential metrics such as share of gross value added or others.

Further work focus on the application of this methodology to more case studies, particularly for cities in completely different stages of the development. The main constraint for this would be difficulty in collecting data for less developed metropolitan areas. However, if successful, this work would allow performing a more detailed and complete analysis on how the consumption of specific materials and by specific sector varies with economic development.

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8. Appendixes

Appendix A - Results for DMC and DMI

2000 Agriculture and mining BN prod. Chem & fuel prod. Construc. Prod. Metal. Prod. Machin.&Eq. Utilities Construction Services FC GFCF Exports DMI DMC

Lille 0,27 0,26 1,76 0,62 0,49 0,36 0,85 0,63 1,71 5,13 1,58 2,66 16,31 13,66 Lyon 0,27 0,17 3,84 0,61 0,36 0,49 1,15 0,84 2,53 5,13 2,11 5,01 22,51 17,51 Paris 0,30 0,20 1,47 0,29 0,20 0,49 0,72 0,78 2,92 5,13 1,96 2,02 16,48 14,46 Berlin 0,18 0,32 0,19 0,19 0,10 0,22 2,61 1,09 7,42 6,09 1,86 0,87 21,14 20,26 Frankfurt 0,30 0,49 0,71 0,19 0,03 0,30 2,01 0,74 7,25 6,09 1,26 2,41 21,78 19,37 Hamburg 0,38 0,18 4,24 0,29 0,13 0,20 2,06 0,83 7,06 6,09 1,42 8,25 31,11 22,86 Lisbon 0,38 0,51 0,36 0,44 0,17 0,39 0,28 0,78 3,69 3,71 5,75 1,48 17,94 16,46 Porto 0,44 1,60 1,09 0,45 0,38 1,21 0,21 1,26 3,31 3,71 9,30 9,29 32,24 22,96 Madrid 0,63 0,42 1,23 0,03 0,89 4,79 0,04 0,71 3,33 4,12 1,80 2,20 20,19 18,00 Liverpool 0,06 0,23 0,22 0,02 0,25 0,51 0,05 0,33 1,63 4,50 0,51 1,94 10,24 8,30 Manchester 0,02 0,38 1,00 0,05 0,38 0,81 0,05 0,47 2,33 4,50 0,56 3,08 13,62 10,54 Stockholm 0,28 0,53 0,69 0,10 0,45 0,65 0,53 2,04 6,55 4,67 2,70 3,40 22,59 19,19 2011 Lille 0,06 0,54 1,69 0,43 0,49 0,35 0,45 0,53 1,54 4,78 1,67 2,95 15,49 12,53 Lyon 0,03 0,41 3,71 0,44 0,36 0,45 0,59 0,67 2,26 4,78 1,68 5,45 20,84 15,39 Paris 0,02 0,30 0,97 0,21 0,14 0,38 0,53 0,56 2,47 4,78 1,41 1,28 13,05 11,77 Berlin 0,22 0,26 0,14 0,20 0,15 0,22 1,30 0,78 6,83 6,60 1,16 1,09 18,94 17,86 Frankfurt 0,22 0,28 0,24 0,17 0,19 0,23 1,34 0,75 6,79 6,60 1,12 1,51 19,44 17,93 Hamburg 0,83 0,31 2,66 0,29 0,19 0,35 1,21 0,73 6,64 6,60 1,09 6,29 27,19 20,90 Lisbon 0,39 0,65 0,41 0,32 0,12 0,37 0,28 0,95 4,79 3,62 7,06 1,40 20,37 18,97 Porto 0,25 0,83 0,62 0,35 0,22 0,60 0,14 0,83 2,81 3,62 6,17 3,47 19,92 16,45 Madrid 0,22 0,21 1,03 0,01 0,63 3,78 0,04 0,48 2,73 2,84 0,93 2,69 15,61 12,91 Liverpool 0,00 0,06 0,57 0,01 0,01 0,02 0,46 0,09 2,23 4,60 0,27 0,91 9,23 8,32 Manchester 0,00 0,10 0,44 0,02 0,01 0,02 0,96 0,13 2,42 4,60 0,37 1,36 10,42 9,05 Stockholm 0,21 0,45 0,71 0,24 0,29 1,53 0,42 1,85 6,64 4,84 4,84 3,71 25,72 22,02

Appendix A - Results for DMC and DMI (Continuation)

2000 Fossil fuels Metallic Non- Biomass Chemicals Others 2011 Fossil fuels Metallic Non- Biomass Chemicals Others minerals metallic and minerals metallic and minerals fertilizers minerals fertilizers Lille 2,16 0,70 6,59 3,94 0,27 0,01 Lille 1,94 0,60 5,86 3,89 0,23 0,01

Lyon 2,49 0,92 9,52 4,26 0,31 0,01 Lyon 2,16 0,75 8,17 4,06 0,24 0,01

Paris 2,38 0,79 6,64 4,39 0,26 0,01 Paris 2,06 0,65 4,83 4,01 0,21 0,01

Berlin 6,05 1,02 8,46 4,42 0,30 0,01 Berlin 5,20 0,86 6,16 5,23 0,39 0,01

Frankfurt 5,72 1,01 8,02 4,32 0,30 0,01 Frankfurt 5,19 0,86 6,36 5,14 0,36 0,01

Hamburg 6,30 1,28 10,73 4,23 0,30 0,01 Hamburg 5,43 0,93 8,82 5,30 0,41 0,01

Lisbon 2,45 0,39 10,32 3,16 0,14 0,01 Lisbon 2,72 0,43 12,30 3,37 0,14 0,01

Porto 2,44 0,48 15,79 4,02 0,21 0,01 Porto 2,10 0,37 10,65 3,17 0,16 0,01

Madrid 3,63 1,02 9,09 3,89 0,36 0,01 Madrid 3,01 0,78 6,03 2,81 0,27 0,01 Liverpool 3,72 0,38 0,92 3,11 0,16 0,01 Liverpool 3,14 0,29 1,57 3,15 0,16 0,01 Manchester 4,65 0,46 1,84 3,38 0,20 0,01 Manchester 3,34 0,33 1,94 3,26 0,17 0,01 Stockholm 2,64 3,21 6,31 6,78 0,23 0,02 Stockholm 2,27 4,31 7,78 7,23 0,42 0,01

GDP/DMI Lille Lyon Paris Berlin Frankfurt Hamburg Lisbon Porto Madrid Liverpool Manchester Stockholm

2000 1241,67 1230,41 2311,21 1076,39 1928,86 676,58 1001,31 377,023 1054,78 2059,83 1784,66 1974,24 2011 1776,35 1784,77 3943,27 1543,75 2564,47 952,34 1148,22 776,11 1986,49 2471,03 2342,75 2332,42

Appendix B – Levels of disaggregation of Metropolitan Areas

Paris, Lille & Lyon Berlin, Hamburg & Madrid Liverpool & Manchester Lisbon & Porto Stockholm Stockholm Description (2000/2011) Frankfurt (2000/2011) (2000/2011) (2000/2011) (2000/2011) (2000) (2011) S01 Agriculture, Hunting, Forestry and Fishing S02 Mining and Quarrying S03 Food, Beverages and Tobacco S04 Textiles and Textile Products S05 Leather, Leather and Footwear S06 Wood and Products of Wood and Cork S07 Pulp, Paper, Paper , Printing and Publishing S08 Coke, Refined Petroleum and Nuclear Fuel S09 Chemicals and Chemical Products S10 Rubber and Plastics S11 Other Non-Metallic Mineral S12 Basic Metals and Fabricated Metal S13 Machinery, Nec S14 Electrical and Optical Equipment S15 Transport Equipment S16 Manufacturing, Nec; Recycling S17 Electricity, Gas and Water Supply S18 Construction S19 Sale, Maintenance and Repair of Motor Vehicles and Motorcycles; Retail Sale of Fuel S20 Wholesale Trade and Commission Trade, Except of Motor Vehicles and Motorcycles S21 Retail Trade, Except of Motor Vehicles and Motorcycles; Repair of Household Goods S22 Hotels and Restaurants S23 Inland Transport S24 Water Transport S25 Air Transport S26 Other Supporting and Auxiliary Transport Activities; Activities of Travel Agencies S27 Post and Telecommunications S28 Financial Intermediation S29 Real Estate Activities S30 Renting of M&Eq and Other Business Activities S31 Public Admin and Defence; Compulsory Social Security S32 Education S33 Health and Social Work S34 Other Community, Social and Personal Services S35 Private Households with Employed Persons Total Number of Levels 27 21 33 32 35 32 29 Sectors aggregated

Appendix C – Validation assessment

Results with application of the method

Agriculture Biomass Chemicals and Construction Metallic Machinery Final 2011 Sectors Utilities Construction Services GFCF Exports DMI & mining products fuel products products products & eq. consumption 35 0,39 0,65 0,41 0,32 0,12 0,37 0,28 0,95 4,79 3,62 7,06 1,40 20,37 Lisbon 25 0,39 0,65 0,41 0,32 0,12 0,37 0,28 0,95 4,00 3,62 7,06 1,45 19,64 35 0,25 0,83 0,62 0,35 0,22 0,60 0,14 0,83 2,81 3,62 6,17 3,47 19,92 Porto 25 0,25 0,83 0,62 0,35 0,22 0,60 0,14 0,83 3,35 3,62 6,17 3,33 20,31 35 0,30 0,08 1,52 0,38 0,20 0,49 0,72 0,78 2,97 5,13 1,96 1,86 16,38 Paris 27 0,25 0,83 0,62 0,35 0,22 0,60 0,14 0,83 3,35 3,62 6,17 3,33 16,48

2011 Sectors Fossil fuels Metallic minerals Non-metallic minerals Biomass Chemicals and fertilizers Others DMC Lisbon 35 2,27 0,38 10,93 3,24 0,16 0,01 16,98 25 2,10 0,37 10,57 3,17 0,16 0,01 16,37 Porto 35 2,50 0,42 11,80 3,32 0,14 0,01 18,19 25 2,72 0,43 12,24 3,37 0,14 0,01 18,91 Paris 35 2,38 0,79 6,64 4,39 0,26 0,01 14,46 27 2,42 0,80 6,74 4,30 0,26 0,01 14,52

Variations

∆ Agriculture Biomass Chemicals and Construction Metallic Machinery and Utilities Construction Services Final GFCF Exports DMI and mining products fuel products products products equipment consumption Lisbon 0,0% 0,0% 0,0% 0,0% 0,0% 0,0% 0,0% 0,0% 16,4% 0,0% 0,0% -3,9% 3,6% Porto 0,0% 0,0% 0,0% 0,0% 0,0% 0,0% 0,0% 0,0% -19,1% 0,0% 0,0% 4,1% -2,0% Paris 0,0% -139,7% 3,3% 21,9% 0,0% 0,0% 0,0% 0,0% 1,6% 0,0% 0,0% -9,0% -0,6% ∆ FF MM NM BM CF Others DMC Porto -8,1% -2,8% -3,5% -2,1% -0,4% -2,3% -3,8% Lisbon 8,2% 3,5% 3,6% 1,4% 1,0% 2,9% 3,8% Paris 1,4% 1,1% 1,6% -2,0% -0,3% 1,2% 0,4%