The sole responsibility for the content of this report lies with the authors. It does not necessarily reflect

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URBAN-WASTE – 690452

D2.1

URBAN-WASTE

Urban Strategies for Waste Management in Tourist Cities

D2.1

Literature Review on Urban Metabolism Studies and Projects

Abstract:

Grant Agreement No: 690452 Project Acronym: URBAN-WASTE Project Title: Urban Strategies for Waste Management in Tourist Cities Contractual delivery date: 31/08/2016 Actual delivery date: 31/08/2016 Contributing WP: WP 2 Dissemination level: Public Editors: Roland Ramusch & Gudrun Obersteiner XXX Contributors: XXX

This report gives a comprehensive review on previous urban metabolism studies in order to

identify and compare methodologies, and provide knowledge on which indicator sets and

background data are suitable for linking tourism activities with waste and use of resources. In most

of the reviewed literature on urban metabolism, waste is included as an indicator in various ways.

However, it has proven complicated to estimate material flows from tourism separately.

It is recommended, that a selection of complementary approaches is applied in order to meet the

different objectives of the project. A combination of MFA and LCA can provide a descriptive ap-

proach to map the current state and also to enable scenario analysis for future planning and policy

making and allows an environmental assessment of the current systems and future scenarios.

The results of waste as a function of tourism shows, that a top-down approach in the data

collection is proposed. This has influence on the development of a template for data collection in

the eleven pilot cities.

This document includes also starting points for WP3: especially the section on behavioural

analysis and gender aspects.

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Document History:

Version Date Editor Modification

Contributors:

Name Company Contributions include

Gudrun Obersteiner

Universität für Bodenkultur Wien Chapter 1, Chapter 2, Chapter 3.3, Chapter 4, Chapter 5, Chapter 6

Roland Ramusch

Iris Gruber

Arie Romein Technische Universiteit Delft (Delft University of Technology)

Chapter 3.2, Chapter 6 Erik Louw

Mattias Eriksson Sveriges Lantbruksuniversitet – Swedish University of Agricultural Sciences

Chapter 3.3.7, Chapter 6

Christian Fertner University of Copenhagen Chapter 3.1, Chapter 6

Juliane Große

Trine Bjørn Olsen Aarhus University - AU Herning Chapter 6

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D2.1 Literature Review on Urban Metabolism Studies and Projects

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Content

1 INTRODUCTION .................................................................................................................................................. 6

2 METHODS ........................................................................................................................................................... 7

3 STATE OF RESEARCH ........................................................................................................................................... 8

3.1 WASTE AND TOURISM FROM AN URBAN METABOLISM PERSPECTIVE.............................................................. 8

3.1.1 Methodological approaches ....................................................................................................................... 9

3.1.1.1 Material- and energy-flow-analysis (MFA / EFA) ............................................................................ 9

3.1.1.2 Ecological footprint (EF) and Life-cycle analyses (LCA) .................................................................. 13

3.1.1.3 Drives-Pressures-State-Impact-Response (DPSIR) ......................................................................... 14

3.1.1.4 Global interrelations, value chains, telecoupling etc. ................................................................... 16

3.1.1.5 Sustainability and quality of life in Urban Metabolism ................................................................. 18

3.1.2 Which role does waste and tourism play in UM studies? ......................................................................... 22

3.1.2.1 Background Data and important/critical issues ............................................................................ 25

3.1.2.2 Gender aspects................................................................................................................................ 26

3.1.2.3 Sub-conclusions ............................................................................................................................... 27

3.2 WASTE BEHAVIOUR AND MANAGEMENT ........................................................................................................ 28

3.2.1 Waste generation and treatment in the European Union ........................................................................ 29

3.2.1.1 Attitudes towards waste generation ............................................................................................... 33

3.2.1.2 Waste management behaviour ....................................................................................................... 37

3.2.2 WASTE GENERATION BY TOURISM ........................................................................................................... 40

3.2.3 WASTE BEHAVIOUR .................................................................................................................................. 41

3.2.4 Environmental Management Systems (EMS) in the tourist sector and the hospitality industry .............. 43

3.2.5 LOCAL AND REGIONAL POLICY FRAMEWORK ........................................................................................... 49

3.3 WASTE GENERATION AS A FUNCTION OF TOURISM ........................................................................................ 50

3.3.1 Introduction .............................................................................................................................................. 50

3.3.2 Tourism's three main impact areas .......................................................................................................... 53

3.3.3 Touristic processes .................................................................................................................................... 53

3.3.4 Scope of the reviewed literature .............................................................................................................. 55

3.3.5 Waste types covered and indicator sets used .......................................................................................... 63

3.3.6 Quantified Indicator Results ..................................................................................................................... 79

3.3.7 Food waste ................................................................................................................................................ 83

3.3.7.1 Waste related data used ................................................................................................................ 84

3.3.7.2 Tourist Indicator Sets ...................................................................................................................... 84

3.3.7.3 Critical processes............................................................................................................................. 85

3.3.8 Waste management issues on islands ...................................................................................................... 87

3.3.9 Gender aspects ......................................................................................................................................... 89

4 LESSONS LEARNED FROM CASE STUDIES FROM PARTNER CITIES ...................................................................... 91

4.1 FLORENCE/ITALY ............................................................................................................................................... 91

4.1.1 Waste-Less in Chinati ................................................................................................................................ 91

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4.1.2 RES MAR – Marine environmental defence .............................................................................................. 93

4.1.2.1 Typologies of tourist municipalities ................................................................................................. 93

4.2 KVALA/GREECE ................................................................................................................................................. 94

4.2.1 WASTE-C-CONTROL .................................................................................................................................. 94

4.2.2 Evaluation and Optimisation of the ISWM system of Kavala.................................................................... 95

4.2.2.1 Assessment indicators ..................................................................................................................... 95

4.3 LISBON/PORTUGAL ........................................................................................................................................... 97

4.3.1 MFA Case Study of the Lisbon Metropolitan Area using the Urban Metabolism Analyst Model ............. 97

4.4 NICOSIA CYPRUS ............................................................................................................................................... 98

4.4.1 Guidelines for meeting the Cyprus Tourism Organisation minimum standards for sustainability in hotel

establishments ........................................................................................................................................................ 98

4.4.2 Waste Mapping Guidance for Hotels in Cyprus ........................................................................................ 98

4.5 PONTA DELGADA / AZORES .............................................................................................................................. 99

4.5.1 SIET-MAC project System of Sustainable Tourism Indicators in Macaronesia ......................................... 99

4.6 TENERIFE / SPAIN ............................................................................................................................................ 100

4.6.1 Environmental performance in the hotel sector: the case of the Western Canary Islands. ................... 100

5 COMPILATION OF EXISTING INDICATORS AND NECESSARY BACKGROUND DATA FROM PREVIOUS STUDIES . 101

6 CONCLUSIONS FOR URBAN-WASTE ASSESSMENT CRITERIA ........................................................................... 109

6.1 LITERATURE REVIEW ....................................................................................................................................... 109

6.2 METHODOLOGY FOR DATA COLLECTION ON WASTE AND TOURISM ............................................................ 109

REFERENCES ............................................................................................................................................................ 119

APPENDIX ................................................................................................................................................................ 129

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List of Figures + Tables FIGURE 1: MULTIPLE SCALES AND DISCIPLINES OF URBAN METABOLISM (ZHANG ET AL. 2015) ................................................................ 8

FIGURE 2: LINEAR AND CIRCULAR URBAN METABOLISM (GIRARDET 2008; LEDUC AND VAN KANN 2013) ................................................ 9

FIGURE 3: MATERIAL FLOW ACCOUNTING (MFA) AT THE URBAN LEVEL (ROSADO ET AL. 2016) ........................................................... 12

FIGURE 4: ECOLOGICAL FOOTPRINT OF AN AVERAGE CANADIAN (WACKERNAGEL AND REES 1995) ....................................................... 13

FIGURE 5: LIFE-CYCLE CHAIN: EXTRACTION – PRODUCTION – CONSUMPTION – WASTE (EEA 2010) ...................................................... 14

FIGURE 6: DPSIR FRAMEWORK USED BY THE EUROPEAN ENVIRONMENT AGENCY (EEA 2007) ........................................................... 15

FIGURE 7: A DPSIR-FRAMEWORK FOR URBAN METABOLISM (FERRÃO AND FERNÁNDEZ 2013) ............................................................ 16

FIGURE 11: ADDITIONAL ELEMENTS OF AN EXPANDED URBAN METABOLISM FRAMEWORK (PINCETL ET AL. 2012) .................................... 20

FIGURE 12: EXTENDED CONCEPT FOR URBAN METABOLISM (MINX ET AL. 2011) ............................................................................... 20

FIGURE 13: HEADLINE INDICATOR SET FOR THE FOUR PROPOSED DIMENSIONS (URBAN FLOWS, URBAN QUALITY, URBAN PATTERNS, URBAN

DRIVERS) ............................................................................................................................................................... 21

FIGURE 14: EXTENDED URBAN METABOLISM FRAMEWORK (NEWMAN 1999) ................................................................................... 23

FIGURE 15: MODEL FOR THE ANALYSIS OF AN URBAN METABOLIC SYSTEM BASED ON THE ROLES PLAYED BY DIFFERENT METABOLIC ACTORS

(ZHANG ET AL. 2013) .............................................................................................................................................. 23

FIGURE 16: WASTE MANAGEMENT AND RECOVERY MATERIAL FLOWS (ECKELMAN AND CHERTOW 2009) .............................................. 25

FIGURE 18 TOTAL WASTE GENERATION BY HOUSEHOLDS IN EUROPE IN 2012 IN KILOGRAMS PER CAPITA BY COUNTRY. .............................. 30

FIGURE 19: COMPARISON BETWEEN THE WASTE GENERATION BY HOUSEHOLDS AND MUNICIPAL WASTE GENERATION IN EUROPE IN 2012 IN

KILOGRAMS PER CAPITA BY COUNTRY. .......................................................................................................................... 31

FIGURE 20: MUNICIPAL WASTE TREATMENT BY TYPE OF TREATMENT AND COUNTRY IN 2013 (IN % OF TOTAL WASTE TREATMENT). ............ 33

FIGURE 21: SHARE OF RESPONDENTS THAT TOTAL AGREE WITH STATEMENTS MY HOUSEHOLD IS GENERATING TOO MUCH WASTE AND MY

COUNTRY IS GENERATING TOO MUCH WASTE BY COUNTRY IN 2014. .................................................................................. 34

TABLE 1: PERCENTAGE TOTAL ‘AGREE’ (TOTAL OF TOTALLY AND TEND TO AGREE) FOR THE FOLLOWING .................................................. 35

FIGURE 22: CORRELATION BETWEEN THE AMOUNT OF WASTE GENERATED BY HOUSEHOLDS (2012) AND THE STATEMENT THAT MY HOUSEHOLD

IS GENERATING TO MUCH WASTE BY COUNTRY (2014). .................................................................................................. 37

FIGURE 23: PERCENTAGE OF RESPONDENTS THAT REDUCE WASTE AND SEPARATE MOST OF WASTE FOR RECYCLING IN 2014 BY COUNTRY. .... 38

TABLE 2: PERCENTAGE RESPONDENTS WHO SEPARATE MOST OF THEIR WASTE AND WHO REDUCE WASTE BY SEX, AGE AND EDUCATION IN 2014.

........................................................................................................................................................................... 39

TABLE 3: DEVELOPMENT OF GLOBAL INTERNATIONAL TOURIST ARRIVALS 1950 – 2015;SOURCE: WORLD TOURISM ORGANISATION (2016) 50

TABLE 4: OVERVIEW OF THE REVIEWED LITERATURE ...................................................................................................................... 58

TABLE 5: OVERVIEW OF WASTE RELATED INDICATOR SETS USED, WASTE TYPES COVERED AND RESULTS OF REVIEWED LITERATURE ................ 66

FIGURE 24: BOXPLOT OF THE DISTRIBUTION OF THE INDICATOR “WASTE GENERATED PER TOURIST AND DAY (N=50) ................................. 79

FIGURE 25: DISTRIBUTION OF 50 DATASETS ON TOURIST WASTE GENERATION ................................................................................... 81

FIGURE 26: BENCHMARKS OF TOURIST WASTE GENERATION FOT DIFFERENT HOTEL TYPES .................................................................... 81

FIGURE 27: WASTE COMPOSITION IN HOTEL ROOMS IN HONG KONG .............................................................................................. 82

TABLE 6: INDEXES AND INDICATORS USED FOR THE EVALUATION OF MUNICIPAL WASTE MANAGEMENT SYSTEMS....................................... 96

TABLE 7: WASTE RELATED DATA REQUIREMENTS ....................................................................................................................... 102

TABLE 8: DATA ON FACTORS OF INFLUENCE .............................................................................................................................. 103

TABLE 9: TOURISM RELATED DATA REQUIREMENTS (EUROSTAT TOURISM STATISTICS) ....................................................................... 104

TABLE 10: LIST OF ASPECTS RELEVANT TO UNDERSTAND WASTE GENERATION IN CONNECTION WITH TOURISM THAT SHOULD BE COVERED BY A

QUALITATIVE DESCRIPTION ...................................................................................................................................... 107

TABLE 11: BOTTOM-UP AND TOP-DOWN APPROACHES RELATED TO THE REVIEWED LITERATURE .......................................................... 111

FIGURE 28: DIFFERENT METHODOLOGICAL APPROACHES IN ASSESSING WASTE AND TOURISM ............................................................. 113

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FIGURE 29: CROSS-SECTIONAL WASTE DATA (SCHEMATICALLY) ..................................................................................................... 113

FIGURE 30: SCHEMATIC TIME SERIES BASED ON ANNUAL DATA AND MONTHLY DATA ......................................................................... 114

FIGURE 31: SCHEMATIC TIME SERIES WITH WINTER AND SUMMER TOURISM SEASON BASED ON MONTHLY DATA ..................................... 114

FIGURE 32: PANEL DATA FOR MUNICIPALITIES WITH WINTER AND SUMMER TOURISM SEASON (SCHEMATICALLY) .................................... 115

TABLE 12: APPROACHES FOR ESTIMATING TOURISM-RELATED WASTE GENERATION AND COLLECTION SORTED BY ANALYSED UNIT AND TIME

SCALE .................................................................................................................................................................. 116

TABLE 13: DEFINITIONS FOR CATALOGUE OF INDICATORS AND BACKGROUND DATA REQUIREMENTS ..................................................... 129

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1 Introduction

Tourism has a high impact related to different aspects, on the one hand it is a worldwide important economic sector, 10% of the world´s GDP is directly or indirectly generated by the tourism sector, one out of eleven jobs are related to tourism. Beside the economic implications, 1.1 billion tourists every year have environmental impacts – beside emissions from transport and the impacts of all necessary infrastructure (airports, hotels etc.) there is a high impact on natural resources (renewable and non-renewable), incl. water resources. It is therefore important that the tourism industry continues to improve and adapt its operation towards waste minimization; following that, waste should be collected, transported and disposed of in an environmentally sound and cost-effective manner. Improper management of waste can lead to substantial and irreversible environmental impacts, such as increases in greenhouse gas emission, land degradation, resource deprivation, surface and groundwater water pollution or loss of biodiversity.

In comparison with other cities, tourist cities have to face additional challenges related to waste prevention and management due to their geographical and climatic conditions, the seasonality of tourism flow and the specificity of tourism industry and of tourists as waste producers. One major objective of the UrBAN-WASTE project is to support policy makers in answering these challenges and in developing strategies that aim at reducing the amount of municipal waste production and at further support the re-use, recycle, collection and disposal of waste in tourist cities.

The concept of urban metabolism will be used to understand and analyze how cities that are influenced by tourism use their resources and how touristic activities are linked to waste management and resource conservation. Therefore UrBAN-WASTE will perform a metabolic analysis of the state of art of urban metabolism in 11 pilot cities.

As first procedural step to meet the projects objectives the development of a proper methodology and the adjustment and definition of data requirements is envisaged. Metabolism indicator sets and a database for the selected pilot tourist cities shall be developed.The database focusses on the touristic processes and the link to resource use, waste generation, prevention, recycling, waste treatment and disposal activities. The database provides the information in order to analyse how tourism is responsible for positive and negative impacts considering the three pillars of sustainability (environment, society and economy).

This deliverable is a report where methodologies, indicator sets and needed background data are reviewed related to urban metabolism studies and waste management linked to the tourism sector. An important part is related to obtaining information on what data / indicator sets are suitable, practicable and comparably easy to obtain. The review will provide knowledge on what data / indicator sets are most suitable for linking touristic activities with waste generation and resource use by a comprehensive review about previous urban metabolism studies and research dealing with tourism.

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2 Methods

To compare and assess at the one hand different methodological approaches in order to find the best tools that will be used in the following steps of the project and at the other hand to find out what indicator sets are currently used and what data are needed for these indicator sets the literature review was split into three main parts also taking into account the practicability of data sets (including for different spatial scales and time periods).

First of all literature was screened focussing on waste and tourism from the Urban Metabolsim perspective. In this section different approaches and methods used to conceptualise and operationalise urban metabolism and how these tackle issues of waste, more specific from touristic activities were reviewed.

Secondly the key words waste behaviour and management were analysed to focus on main issues about waste behaviour and management. This was done by firstly looking for review papers and from this on looking at more recent papers.

The last part focusses on studies dealing with waste generation as a function of tourism. The papers where analysed and classified in a MS Excel file according to certain categories, allowing a subsequent analysis and illustration based on different characteristics.

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3 State of Research

3.1 Waste and tourism from an Urban Metabolism perspective

The concept of urban metabolism (UM) was developed by Wolman (1965). Kennedy et al. (2007) define urban metabolism as “the sum total of the technical and socioeconomic processes that occur in cities, resulting in growth, production of energy, and elimination of waste”. Waste, and therewith waste from tourists occurring in the urban sphere, are main components of urban metabolism. In this section we review different approaches and methods used to conceptualise and operationalise urban metabolism and how these tackle issues of waste, more specific from tourist activities.

Figure 1: Multiple scales and disciplines of urban metabolism (Zhang et al. 2015)

Depending on the approach chosen, the analysis of urban metabolism can be used for four purposes (Kennedy et al. 2011):

1. provision of sustainability indicators

2. provision of inputs to urban greenhouse gas (GHG) accounting

3. provision of dynamic mathematical models for policy analysis

4. development of design tools.

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The focus of this study lies on point 1, to provide knowledge on sustainability indicators for further use in the Urban Waste project. However, the comprehensive and inclusive concept of urban metabolism has the potential to analyse waste and tourism in a systemic and impact oriented way, which can also be used for policy advice and which is why we chose to use an urban metabolism perspective (besides others) in workpackage 2 of the project. An important aspect is therefore the sustainability dimension within urban metabolism. How can cities reduce resource consumption and minimize waste and emissions while improving or maintaining the quality of life of their citizens (and visitors). A recurring idea is the move from a linear to circular urban metabolism and urban economy, as illustrated in Figure 2.

Figure 2: Linear and circular urban metabolism (Girardet 2008; Leduc and Van Kann 2013)

3.1.1 Methodological approaches

In the following we will review different approaches to urban metabolism and how they have been applied.

3.1.1.1 Material- and energy-flow-analysis (MFA / EFA)

A recent review of urban metabolism methodologies (Zhang 2013; Zhang et al. 2015) distinguishes “two main accounting and assessment methods for urban metabolism [that] are based on an analysis of material and energy flows”: “Material-flow-analysis” (MFA) or “mass balance” has the

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goal to provide a system level understanding of how a city, region or nation functions (Holmes and Pincetl 2012). MFA traces the input, storage, transformation, and output processes and it allows following the material flows throughout the life cycle within an urban system, based on the physical principle that matter can neither be created nor destroyed. MFA also allows for comparisons across cities and inputs (Pincetl 2012). A concept strongly related to MFA, but using a different methodology is substance-flow-analysis (SFA). SFA “observes the changes in the substance flows among different life-cycle stages”, whereas MFA, in contrast, “investigates the quantity and state of cross-sectional data at different life-cycle stages” (Zhang et al. 2015) and is a such an external and static analysis. However, there is no standardized method for SFA (Barles 2010).

A second approach, which is a modification of the MFA framework, is “energy-flow-analysis” (EFA) or “energy balance”. It was developed to provide a more detailed understanding of urban metabolic processes (Zhang 2013). A further development of EFA is the concept of “emergy” and “exergy”, which represent embodied energy and “the amount of useful work that can be performed by the energy in a system” (Zhang 2013; Zhang et al. 2015). This concept allows integrating material flows with different measurement units. Emergy provides a method for studying the energetic flows in a socio-economic system and can also provide a comparative tool to understand “the relative work of other materials flowing through a socio-economic system” (Holmes and Pincetl 2012). However, MFA has long been favoured over EFA which resulted in overlooking major environmental and social issues (Barles 2010; Holmes and Pincetl 2012).

Within the framework of MFA and EFA different simulation models are used for the quantitative analysis of the metabolic flows of an urban system, such as the ecological network analysis (ENA) or input-output-analysis (Zhang 2013; Zhang et al. 2015). In a case study on Beijing, Zhang et al. (2013) conduct a MFA by applying network theory for the years 1998-2007. The authors divide Beijing’s urban metabolic system into seven components (agriculture, materials and energy transformation, mining, recycling, domestic consumption, processing and manufacturing, construction) and describe the flows between these components by linking them in a network model. Additionally, they define six input and six output paths with the environment. They furthermore introduce four metabolic indicators (metabolic scale, intensity, efficiency and impact) to assess the structural characteristics of the metabolic system and each of its components.

A case study on Paris (Barles 2010) uses Local Bulk Material Balance to conduct a MFA at three different scales (Paris, Paris and its inner suburbs (PPC) and Paris Greater Metropolitan Region (Île-de-France)). Material Balance uses balancing inputs (BI) and balancing outputs (BO) to balance the MFA. The balancing inputs and outputs can be defined by different indicators, such as Total Material Requirement (TMR), Total Material Input (TMI), Direct Material Input (DMI), Net Addition to Stock (NAS), Direct Processed Output (DPO), Local and Exported Processed Output (LEPO), Total Domestic Output (TDO), Direct Material Output (DMO), Total Material Output (TMO).

By means of a case study on Lisbon Metropolitan Area, Rosado et al. (2014) developed a new simulation model for MFA, the urban metabolism analyst (UMAn). The model provides values for materials accounting, throughput over time, distribution by economic activity, and spatial distribution. UMAn thereby “associates material flows with economic activities and their spatial

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location within the urban area” (Rosado et al. 2014) and allows to bridge seven methodological gaps in previous urban metabolism studies:

lack of a unified methodology

lack of material flows data at the urban level

limited categorizations of material types

limited results about material flows as they are related to economic activities; limited understanding of the origin and destination of flows

lack of understanding about the dynamics of added stock

lack of knowledge about the magnitude of the flow of materials that are imported and then, to

a great extent, exported (Rosado et al. 2014)

In a further study on three metropolitan areas in Sweden (Rosado et al. 2016) – using material-flow-analysis and the urban metabolism analyst (UMAn) simulation model – the authors adapt the Economy Wide MFA principles (European Commission, eurostat 2001) by excluding water from the accounting. The study considers the following indicators in the MFA: Direct Material Input (DMI), Imports (Imp), Exports (Exp), Domestic Extraction (DE), Domestic Material Consumption (DMC), Net Addition to Stock (NAS), Industrial Production (IP), Domestic Processed Output (DPO), and Recovery (Figure 3). The study further applies a framework of eight urban metabolism characteristics, which are described by the above named indicators, in order to deduce urban metabolism profiles and describe the resilience of urban areas. The UM characteristics are: Material needs, accumulation of materials, urban metabolism efficiency, diversity of processes, support provided by an urban area, dependency on other systems, self-sufficiency and pressure on the environment.

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Figure 3: Material Flow Accounting (MFA) at the urban level (Rosado et al. 2016)

One first approach to come up with a standardized and comprehensive urban metabolism framework was introduced by Kennedy and Hoornweg (2012), which also includes a list of abbreviated parameters. Their framework builds on the Eurostat material-flow-analysis system (European Commission, eurostat 2001) and combines it with methods of water-, energy- and substance-flow-analysis. The parameters refer to inflows, production, stocks and outflows of biomass, minerals, water and energy. For its application on megacities, Kennedy et al. (2014) adapted their framework in order to focus only on parameters which are major components of urban metabolism, such as energy, water, material and waste flows. Their adapted set of parameters is organized in four layers: (1) Definition of a megacity, (2) biophysical characteristics, (3) aggregate urban metabolism parameters and (4) role of utilities (Kennedy et al. 2014). The framework of Kennedy and Hoornweg (2012) and Kennedy et al. (2014) is applied in a case study of Curitiba (Brazil) by Conke and Ferreira (2015) and their study also includes a social dimension, an extension of the urban metabolism framework that is introduced by Newman (1999).

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3.1.1.2 Ecological footprint (EF) and Life-cycle analyses (LCA)

A main critique of MFA and similar approaches is, that they can hardly be used to evaluate the sustainability of an urban system as they are not (directly) relating to the impact of material (and non-material) flows (Zhang 2013; Zhang et al. 2015). A concept dealing with the impact of urban growth and consumption is the concept of the Ecological footprint (EF). It measures the land area necessary to sustain city’s (or also a person’s) resource consumption and waste discharge. Figure 4 illustrates the main idea, which was broadly introduced by Wackernagel and Rees (1995).

Figure 4: Ecological footprint of an average Canadian (Wackernagel and Rees 1995)

The advantages of EF are that it combines socioeconomic development demands with ecological capacity and that, as mentioned above, can therewith reveal ecologically unsustainable situations. However, problems of the concept are that it neglects the ability of land to provide multiple functions, and that, because of incomplete descriptions of resource provision (and waste discharge) by the natural system, it underestimates human impact (Zhang 2013). Via an expert survey, Wiedmann and Barrett (2010) found that EF

“(a) is seen as a strong communication tool, (b) has a limited role within a policy context, (c) is limited in scope, (d) should be closer aligned to the UN System of Environmental and Economic Accounting and (e) is most useful as part of a basket of indicators.” (Wiedmann and Barrett 2010) A different approach to analyse the impacts of material flows is life-cycle assessment (LCA). According to Pincetl (2012) mass balance analysis (MFA) “can incorporate [LCA] to capture the

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indirect and supply chain impacts of cities beyond their borders and materials flow analysis (MFA)”. By these means MFA and LCA allow cradle-to-grave assessments of the flows in a city’s metabolism.

Figure 5: Life-cycle chain: extraction – production – consumption – waste (EEA 2010)

Additional information on LCA is provided by different authors ((Barles 2010), (Holmes and Pincetl 2012)).

3.1.1.3 Drives-Pressures-State-Impact-Response (DPSIR)

In the 1990s the European Environment Agency developed (based on the PSR model of the OECD) a causal-indicator framework to describe interactions between society and the environment (Figure 6). The DPSIR model (driving forces, pressures, states, impacts, responses) applies a systems-view: “social and economic developments exert pressure on the environment which changes its state. This leads to impacts (human/ecosystem) which may result in responses by the society, which feeds back the other components (EEA 2007).

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Figure 6: DPSIR Framework used by the European Environment Agency (EEA 2007)

Using the model might be most helpful when focusing on the linkages between D-P-S-I-R. Indicators reflecting these are (EEA 2007):

“Eco-efficiency indicators (between D and P). Increasing eco-efficiency means that economic activities can expand without an equivalent increase in pressure on the environment.

Pathways and dispersion patterns link P and S. The combination of these indicators tells a story

of time delay in natural processes and the ‘time bombs’ created in the environment. Knowledge of dispersion patterns can be useful to model current and future changes in the state of the environment and in impacts.

Dose/response relationships link S to I. Knowledge of dose/response relationships can be used to predict or quantify the health impacts of pollution, or help in choosing the most appropriate

state indicator to act as an early warning.

Economic costs of the impact and other indicators that confirm societal perception of the

seriousness of the impacts are key for triggering societal responses. These highlight the link between I and R.

Policy-effectiveness indicators generally summarise the relations between the response and targets for expected change in driving forces or pressures and sometimes in responses, state or even impacts. ” (EEA 2007)

However, the model was critized for not considering social and socio-economic aspects very well (Svarstad et al. 2008). Tscherning et al. (2012) reviewed the potential of DPSIR to support decision making, by applying two main criteria: (1) the development of conceptual models integrating knowledge from different disciplines, specialists and policy makers, as well as those affected by their decisions; and (2) the potential to explain the results and analysis of research to different disciplines, specialists, stakeholders and the public and to demonstrate alternatives and provide decision options. They found that DPSIR can be useful by means of showing solid evidence with alternatives and decision options, rather than by presenting predetermined solutions.

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Figure 7 illustrates another example of the DPSIR approach in an urban context (Ferrão and Fernández 2013). The authors underscore the importance of defining all elements and specifying the relationships between them. However, they also state that the task of defining those has been an ongoing challenge since DPSIR was developed, probably not least because in many occasions end users were not addressed during its development, as Tscherning et al. (2012) notice.

Figure 7: A DPSIR-framework for urban metabolism (Ferrão and Fernández 2013)

3.1.1.4 Global interrelations, value chains, telecoupling etc.

An issue not always addressed by the previous mentioned approaches dealing with urban metabolism are interrelations going beyond the city context (Zhang et al. 2015). In LCA and footprint analysis it is though the ambition to include the impact of the whole product chain until its final consumption. However, some further approaches might be useful to mention in this

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context as they particularly expand on indirect relations, spillover systems, hidden effects or anthropogenic / global metabolism. A widely used concept within the analysis of world trade and production are global value chains (GVC), or supply chain analysis. GVC typically identifies all activities that firms undertake to produce, transform and supply a product (OECD 2012). GVC are thereby especially useful to uncover the global relations and dependencies of specific economic sectors, and cause-effect relations on a global scale.

Figure 8: Global value chain of Nutella (OECD 2012) A concept going even further is “telecoupling”, which aims as increasing our understanding of “how the world functions over distances and identify solutions to achieve socioeconomic and environmental sustainability across local to global levels, because it is uniquely integrative in several ways” (Liu et al. 2013). Telecoupling intergrates socioeconomic and environmental interactions over distances (Figure 9). Also, it includes “spillover systems” in the analysis and ‘hidden’ or ‘indirect’ effects in different locations than where the supply and demand of specific services and products is taking place (Figure 10).

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Figure 9: Definitions of teleconnections, globalisation and telecoupling (Liu et al. 2013)

Figure 10: Relations between sending, receiving in spillover system (Liu et al. 2013)

3.1.1.5 Sustainability and quality of life in Urban Metabolism

As mentioned earlier, a major shortcoming of the established methodologies in the urban metabolism / MFA framework is that they are not suitable to assess the level of sustainability of a system. This deficit is addressed by several studies that aim for an extension of the framework of

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urban metabolism / MFA in order to gain a more comprehensive picture, including components of sustainability and quality of life.

A study addressing interdisciplinary boundaries of urban metabolism (Broto et al. 2012) highlights that politics influence socio-environmental metabolisms and that “the metabolism of the city is not only shaped by visible flows, but also by the ways in which different forms of circulation are imagined and represented through the city” (Broto et al. 2012). The study argues that looking closer at the organization of production and consumption patterns into flows – of materials, energy, people, meanings, and power – is necessary for addressing the challenges of urban sustainability; however, the study does not suggest a respective indicator set.

An important extension of the urban metabolism model was conducted by Newman (1999) by adding a further dimensions of “social metabolism” to the classic model applying a socio-economic perspective. Newman (1999) refers to a social dimension by including the dynamics of settlements and liveability in these settlements in the model. The extended metabolism model applies an indicator set that covers metabolic flows and liveability; the indicators are classified in five groups: (1) energy and air quality, (2) water, materials and waste, (3) land, green spaces and biodiversity, (4) transportation and (5) liveability, human amenity and health. In this extended version the urban metabolism model does not only assess liveability but also sustainability of cities.

Also Kennedy et al. (2011) see a need for considering social, health and economic indicators in urban metabolism models, but in contrast to Newman (1999) they suggest the integration of those indicators instead of only adding them.

Zhang et al. (2015) provide a concept (see Figure 1) for the multiple scales and disciplines that should be considered in urban metabolism in order to get a fuller understanding of it: Beyond the scale of urban metabolism they outline regional metabolism (RM) at the regional scale, social metabolism (SM) at the national scale and anthroposphere metabolism (AM) at the global scale. Minx et al. (2011) also go the other way, looking at sub-city and district scale.

This is also an attempt to compensate for a “fundamental blindness” (Pincetl et al. 2012) of the established urban metabolism methodologies in the inability to attribute flows to places, people or uses; according to them it requires understanding on “who-is-using-what-flows-where-to-do-what”, in other words it lacks a specific spatial reference to energy or material flows.

Pincetl et al. (2012) suggest an expanded urban metabolism framework (see Figure 11) that integrates the existing methodologies and theories, such as LCA, political ecology or ecosystem services, instead of limiting it to one of them in order to provide “comprehensible assessments of energy and material use in cities” (Pincetl et al. 2012).

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Figure 11: Additional elements of an expanded urban metabolism framework (Pincetl et al. 2012)

Pincetl et al. (2012) emphasize the importance of recognizing scalar relationships (geographic specificities) of urban metabolism, as included in their expanded framework. A comprehensive extension of the urban metabolism concept (see Figure 12) is suggested by Minx et al. (2011) including aspects of environmental quality, urban drivers and urban patterns, and urban quality and co-benefits.

Figure 12: Extended concept for urban metabolism (Minx et al. 2011)

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In their study they present two approaches for an extended concept of urban metabolism. The first one – “A simple indicator system for monitoring urban metabolism in Europe” – provides a comprehensive indicator set for each of the four proposed dimensions – urban flows, urban quality, urban patterns and urban drivers –, which are summarized in a headline indicator set (see Figure 13).

Figure 13: Headline indicator set for the four proposed dimensions (urban flows, urban quality, urban patterns, urban drivers)

With the second approach – “Small area estimates for carbon footprints and energy consumption” – the study addresses two main restrictions of the first approach: those are data restrictions related to calculations of consumption-based indicators and the fact that the first approach is only applicable on the administrative city level. The second approach is tested and applied for the UK in the study. The authors use more comprehensive data, comprehensive consumption based estimates of CO2 emissions in the UK, which cover the whole country and allow a downscaling methodology towards smaller spatial scales. The calculation of carbon footprints at smaller spatial scales requires local consumer expenditure data. As this data is usually not available in sufficient sample sizes the authors suggest using geodemographic data, lifestyle classification by clustering, to downscale the data. The model includes scaling and updating procedures to secure consistency through the different scales. The study further includes a validation of the downscaling method by comparing the results when instead using detailed domestic electricity and gas consumption data; which shows reasonable evidence for following up on the downscaling method. Summarizing, the second approach extends the indicator set proposed in the first approach in the following three aspects (Minx et al. 2011):

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“A complete consumption based account has been provided, which covers all indirect CO2

emissions associated with consumption in cities;

Small area estimates of CO2 emissions have been provided not only for urban, but also rural areas;

CO2 emission estimates with a much higher spatial resolution have been provided.”

A methodology developed to study “biophysical and socioeconomic issues in an integrated manner, both for the level of the society and for the different compartments of that society” is the so-called Multi-Scale Integrated Analysis of Societal and Ecosystem Metabolism (MuSIASEM) (Ginard-Bosch and Ramos-Martín 2016). The methodology is a static analysis that measures congruence between flows and funds over several scales (time, space etc.) and thereby allows observing the evolution of a system over time. But being a static analysis is at the same time one of the shortcomings of the methodology as it provides a snapshot of a system but not of its dynamics (Ginard-Bosch and Ramos-Martín 2016). The methodology uses flow and fund indicators, those are in a case study of the Balearic Islands (ibid.) the following: Total energy throughput (TET), total human activity (THA), gross domestic product (GDP), energy throughput in activity i (ETi), human activity in activity i (HAi), GDP per hour in the society (GDPhour), exosomatic metabolic rate, average of the society (MJ/h) (EMRSA), exosomatic metabolic rate (MJ/h) (EMRi), economic labour productivity (h/h) (ELPi), economic energy intensity (MJ/€) (EEIi).

3.1.2 Which role does waste and tourism play in UM studies?

The reviewed studies on urban metabolism methodologies include waste or more specific tourism-related waste in different ways in its respective indicator sets. In their detailed guideline on MFA, Brunner and Rechberger (2004) explain the importance of MFA for waste management because MFA “can cost-efficiently determine the elemental composition of wastes exactly” (Brunner, Paul H. and Rechberger, Helmut 2004) and thereby provide crucial information about the most suitable recycling/treatment technology. Newman (1999) includes waste in his extended framework for urban metabolism as output category by itself (Figure 14).

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Figure 14: Extended urban metabolism framework (Newman 1999)

A case study on Beijing (Zhang et al. 2013) that applies MFA with network theory uses an urban metabolic model that looks separately at resource metabolism and waste metabolism (see Figure 15). Waste metabolism “focuses on the generation, reuse, and final discharge of wastes, and can be described in terms of the environmental impacts of these wastes and their cycling” (Zhang et al. 2013).

Figure 15: Model for the analysis of an urban metabolic system based on the roles played by different metabolic actors (Zhang et al. 2013)

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A case study on Paris (Barles 2010) summarizes waste as Local and Exported Processed Output (LEPO) and distinguishes between local and exported outputs to nature that consist of emissions to air and water, wastes landfilled and dissipative flows. In the study of Kennedy and Hoornweg (2012) that attempts to provide a standardized and comprehensive urban metabolism framework, waste is included both in the categories stocks (landfill waste, construction/demolition waste) and outflows (exported landfill waste, incinerated waste, exported recyclables, wastewater, emissions, pollutants and particulates). In the adapted version of their framework (Kennedy et al. 2014), which is organized in four layers, waste is included in layer 3 – aggregate urban metabolism – as waste water, solid waste disposal on land and waste incineration as well as in layer 4 – role of utilities – in different indicators assessing the quality of service and again as waste water. The comprehensive indicator set that is provided as part of the study of Minx et al. (2011) on an extended concept of urban metabolism considers waste in the dimension of “urban flows” and distinguishes waste intensity of production, residential waste intensity, waste recycling, waste incineration and landfill (Minx et al. 2011). The case study on three Swedish cities (Rosado et al. 2016) categorizes waste a bit different in its applied indicator set: Waste is included in the Domestic Processed Output (DPO), but categorized into air emissions (carbon of fossil fuels origin vs. biomass origin) on the one hand and household vs. industrial waste on the other hand. The study takes thereby a more origin oriented perspective instead of destination (e.g. recyclable) oriented perspective as the other studies. A study dealing particularly with long-term waste management (Eckelman and Chertow 2009), distinguishes waste as part of the outputs in recycled waste on the one hand and landfilled or released into the environment (land, water, air) on the other hand. This study is one of the few that tries to also estimate material flows related to tourism, in consumption and transportation. That is, however, complicated as a distinction between goods and services consumed by tourists vs. residents is difficult.

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Figure 16: Waste management and recovery material flows (Eckelman and Chertow 2009)

Figure 16 illustrates in detail the material flow model used to explain the waste management system, which allows estimating the different types of waste output (e.g. recycling, energy generation, export). Summarizing waste is considered in different ways in the various indicator sets, however, mostly distinguished by its final destination (e.g. recyclable, landfill). Touristic waste is in almost no study addressed separately.

3.1.2.1 Background Data and important/critical issues

Yetano Roche et al. (2014) list some typical data used in urban metabolism studies. Figure 17 lists besides typically used data from MFA/EFA also information on ecological footprint (EF). This combination is used to tackle the drawbacks of both methods. However, socio-economic driving forces, as illustrated in the DPSIR concept of in indicator systems including sustainability aspects should also be included. Also, major contextual factors as climate, infrastructure, resource availability, cultural/historical path-dependency have to be included (e.g. as a city typology) to consider some major limiting/ or enabling differences in the urban systems.

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Figure 17: Examples of basic data requirements for urban metabolism studies (Yetano Roche et al.

2014) However, a major limitation of many UM studies the coarse spatial level used. Shahrokni et al. (2015) even suggest a “Smart urban metabolism”, where they combine real time urban data and illustrate urban metabolism flows on the household and urban district level. In the URBAN WASTE project it will be important to work with spatial explicit data which can be used to differentiate between different types of areas in the city, e.g. those which are important for tourism, specific ecosystems and sensitive areas, or areas contributing differently to the urban economy, urban resource use or pollution etc.

3.1.2.2 Gender aspects

The reviewed studies on urban metabolism do not consider gender aspects explicitly. If they at all consider social aspects, then only on a very general scale. However, some studies as e.g. Minx et al. (2009; 2011) highlight the great potential to differentiate studies by different life styles, as e.g. carbon footprints can be very different in different household types. They use market segmentation from model MOSAIC, developed by the UK company Experian, to differentiate 11 lifestyle groups and 61 lifestyle types. However, gender is not specifically used here, besides that it might be differently represented in the different groups.

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3.1.2.3 Sub-conclusions

Yetano Roche et al. (2014) summarize nicely how different approaches support different questions and recommend a complimentary approach:

“Territorial approaches may help best in understanding urban and regional planning

needs;

Supply-chain approaches may help to identify the role of the process chain;

Whereas consumption-based approaches may reveal policy needs for behavioural and

macroeconomic changes.”

Futhermore we have to decide between the analytical power of approaches, the policy-support potential and not least the feasibility to apply them in the project. E.g. ecological footprint analysis might have great communication potential while the analytical power is limited. MFA/EFA on the other hand have to be combined with aspects of sustainability if they should be used in a later policy discussion. Minx et al. (2011) recommends to

link the urban metabolism to environmental pressures and aspects of environmental quality at multiple scales;

link urban metabolism to urban drivers, patterns and lifestyles;

link urban metabolism to aspects of quality of life.

Measuring synergies and trade-offs between sustainability goals is a way forward, as well as the “coupling of MFA and LCA (connecting mass balances with insights about the varying pressures that different flows might have on the environment).” (Yetano Roche et al. 2014). Important hereby is not to stay on the descriptive approach and try to understand the past only but try to move forward and include modelling and scenario analysis to think about future development. Or as Minx et al. (2011) put it “Ultimately the aim of urban metabolism research is to understand how cities can move from one metabolic state (e.g. uni-directional economy) to another (e.g. circular economy).”

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3.2 Waste behaviour and management

In this section we present and discuss the results of the comprehensive literature about waste behaviour and waste management. The aim was not to make an inventory of the total (scholarly) literature about the subjects but to review the main issues about waste behaviour and management. This was done by firstly looking for review papers and from this on looking at more recent papers. In total we found three relevant review papers (Pirani and Arafat 2014, Myung et al., 2012; Chan and Hsu, 2016). In general it can be concluded that is only a small amount of literature that is aimed at solid waste in the tourism industry. There is more literature about environmental management (systems) and ‘green initiatives’ in the tourism industry. Often these systems and initiatives include actions to minimize solid waste generation and/or management of solid waste in a sustainable way. However in most studies no distinction is made between solid waste and other types of (municipal) waste.

From our comprehensive review we conclude in line with Qian and Schneider (2016: 19) that “To date, research on waste minimization practices within the tourism industry focuses primarily on the hospitality sector, is geographically limited, and addresses practices cross-sectionally”. The same applies for studies about environmental management systems (EMSs). There is hardly any attention to other subsectors of the tourism industry such as for instance campsites or conference venues. Most studies are also case studies, mainly about a city, a region and even a single hotel. There seem to be relative much attention for tourist islands because there the problems with waste generation by tourists are particularly serious (Ezeah et al., 2015). These problems are more pressing where the number of people that stay on the island is more times larger than the resident population. This number is largest in the high season, but Arbulú et al. (2016: 252) calculate approximately 10 tourist per resident per year in Mallorca. They also comment that these islands face high opportunity costs of land needed for waste storage and processing, in particular in case of small geographic areas (op. cit: 257).

Surprisingly there are also very few studies that deal with the result of EMSs (Environmental Management Systems) and green initiatives in terms of the amount of waste that is reduced or recycled. The studies that deal with this issue are either model simulations or aim at best practices.

This section is organised as follows. After a comprehensice summary on European waste statistics (3.2.1) we begin or review with some literature about waste generation by tourism (3.2.2). In 3.2.3 we focus on waste behaviour. By this we mean the behaviour of tourists about waste generation, recycling etc. at their destination. In 3.2.4 we focus on research concerning the implementation of EMSs in the hospitality industry and explain the roles of various stakeholders including employees. Finally, 3.2.5 deals with the local and regional policy framework of waste management in cities and tourist islands. A mature local or regional waste management system should combine these various topics into one integrated waste management plan (UNEP & GTZ, 2003).

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3.2.1 Waste generation and treatment in the European Union

In this chapter we give a brief overview of some basic statistics about waste generation and waste management practices in Europe on a country level. This info gives a general insight into the amount of (solid) waste and the way it is treated. Also information is gathered about the attitudes of European citizens about (their) waste generation and waste management practices. We conclude this chapter with an attempt to relate waste generation and management with tourism.

European statistics about waste make a distinctions between waste generated by economic activities and households. Because there are no waste statistics which focus on tourism, we focus on waste generation by households to give a general impression of the magnitude of waste generation. In 2012 the total annual waste generation per capita in the EU (28 countries) was 4,982 kilograms. Most of the waste comes from economic activities. Households ‘only’ generated 423 kilograms of waste per capita. Across the EU Member States, waste generation by households ranged, in 2012 from an average of 232 kg per capita in Romania to 667 kg per capita in Denmark (see Figure 18). The variations reflect differences in consumption patterns and economic wealth.

There are also variations in the composition of the waste generated by households (see also Figure 18). In 2012 on average in the EU 65% of the household waste is categorised in the category ‘mixed ordinary wastes’. On a country level this share ranged from 23% in Cyprus to 99% in Greece. Two other main waste categories arms recyclable waste (class, paper, metal, etc.) and animal and vegetal wastes (including food waste). Their share in the total household waste generation in the EU in 2012 was resp. 17% and 13%. We find the highest share of recyclable waste in Cyprus (55%) and the lowest share in Greece). These variations not only reflect differences in consumption patterns and economic wealth; it is also conceivable that differences in national statistics and data collection contribute to these variations.

Another EU statistic that sheds light on the waste generation of households is that of municipal waste. These data are widely used for comparing municipal waste generation and treatment in different countries, and as indicators to monitor countries’ waste policies. These data are also often used as a proxy for quantities of (municipal) solid waste (European Environment Agency, 2013). Municipal waste is defined by Eurostat as follows: ‘Municipal waste is mainly produced by households, though similar wastes from sources such as commerce, offices and public institutions are included. The amount of municipal waste generated consists of waste collected by or on behalf of municipal authorities and disposed of through the waste management system’.1

Unlike the general Eurostat waste statistics, its municipal waste statistic provides no information on waste composition. However, it has information over waste treatment and data is available over a longer period of time (1995-current) than the general waste statistic. For Europe as a whole the amount (measured in kilograms per capita) of municipal waste is larger than the amount of waste generated by households. In 2012 the amount of municipal generation waste was 485 kilograms per capita vs 423 kilograms per capita waste generated by households. The differences

1 Eurostat, Reference Metadata in Euro SDMX Metadata Structure (ESMS).

http://ec.europa.eu/eurostat/cache/metadata/en/env_wasmun_esms.htm accessed 1 august 2016.

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by country between these figures are displayed in Figure 19 and show that these are most countries in line with the waste generation by households. The differences in municipal waste generation not only reflects differences consumption patterns and economic wealth, but also on how municipal waste is collected and managed.2

Figure 18 Total waste generation by households in Europe in 2012 in kilograms per capita by country.

2 Eurostat: http://ec.europa.eu/eurostat/statistics-

explained/index.php/Municipal_waste_statistics#Data_sources_and_availability accessed 1 august

2016.

http://ec.europa.eu/eurostat/statistics-explained/index.php/Municipal_waste_statistics#Data_sources_and_availabilityhttp://ec.europa.eu/eurostat/statistics-explained/index.php/Municipal_waste_statistics#Data_sources_and_availability

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Source: Eurostat (Waste Statistics).

Figure 19: Comparison between the waste generation by households and municipal waste generation in Europe in 2012 in kilograms per capita by country.

0 100 200 300 400 500 600 700 800

Romania

Poland

Hungary

Croatia

Slovakia

Czech Republic

Slovenia

Finland

Estonia

Ireland

Malta

Bulgaria

Lithuania

European Union (28 countries)

United Kingdom

Greece

Sweden

Belgium

Portugal

Germany

Spain

France

Luxembourg

Austria

Norway

Italy

Cyprus

Netherlands

Latvia

Denmark

Iceland

Kilograms per capita

Recyclable wastes Animal and vegetal wastes Mixed ordinary wastes Remaining

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Source: Eurostat (Municipal waste statistics).

Differences in the management of municipal waste are show in Figure 20. In 2013 in the EU (28 countries) landfill was the largest municipal waste treatment category (31%), followed by recycling (27%) and incineration (26%). Although landfill is still the largest category its share has dropped significantly from 64% in 1995 to 27% in 2013 (EU-27). Despite this decrease landfill still shows considerable variance between European countries (86% in Malta and 1% in Sweden). For incineration this variance between countries is smaller but still considerable (64 in Estonia to 0% in Latvia, Cyprus, Croatia and Malta). Recycling ranges from 47% in Germany to 5% in Romania.

0 100 200 300 400 500 600 700 800

RomaniaPoland

HungaryCroatia

SlovakiaCzech Republic

SloveniaFinlandEstoniaIreland

MaltaBulgaria

LithuaniaEuropean Union (28 countries)

United KingdomGreece

SwedenBelgiumPortugal

GermanySpain

FranceLuxembourg

AustriaNorway

ItalyCyprus

NetherlandsLatvia

DenmarkIceland

Kilograms per capita

Total municipal waste generation Total waste generated by households

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Figure 20: Municipal waste treatment by type of treatment and country in 2013 (in % of total waste treatment).

Source: Eurostat (Municipal waste statistics).

3.2.1.1 Attitudes towards waste generation

The EU conducts various surveys among its citizens to investigate the motivations, feelings and reactions towards the environment, waste generation and waste management. In the survey about attitudes towards the environment the respondents were asked to select the five environmental issues that worried them most (European Commission, 2014a). In 2014 56% of the respondents stated that they are worried about air pollution, 50% about water pollution, 43%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

SwedenBelgium

GermanyNetherlands

DenmarkNorwayAustriaEstonia

LuxembourgFinlandFrance

European Union (28 countries)United Kingdom

ItalyIreland

SloveniaIceland

PortugalSpain

Czech RepublicPoland

LithuaniaHungaryBulgariaSlovakiaGreece

RomaniaLatvia

CyprusCroatia

Malta

Landfill Incineration Recycling Composting

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about the impact on health of chemicals used in everyday products and 43% about the growing amount of waste. The growing amount of waste worries a relatively high proportion of respondents in the Czech Republic (61%), Hungary (59%), Finland (57%), Croatia (55%) and Slovakia (55%). At the other end of the scale, less than a third of people say they are worried about this in Spain (30%) and the Netherlands (32%). The data show that women are more likely than men to worry about growing amount of waste (46% vs. 41%). Age and education hardly differentiate between the answers. These proportions hardly differ between age groups and levels of education. In a survey about attitudes towards waste management and resource efficiency respondents were asked if they agreed with the statements that their country and households generated too much waste (European Commission, 2014b). In 2014 a majority of the respondents across Europe (87%) considered that their country generates too much waste. Interestingly, only half of this large share, a minority of 43%, believed that their own household did the same. In Figure 21 the outcomes for each country are presented. This figure shows that the range in country shares total ‘agree’ with both statement are the same (27%). However, a higher percentage on one statement did not always corresponds with a higher percentage on the other statement. For The Netherlands and Denmark the difference between the scores on both statements was 28 percent-points, whereas in the United Kingdom and Hungary it was 53 and 54 percent-points.

Figure 21: Share of respondents that total agree with statements my household is generating too much waste and my country is generating too much waste by country in 2014.

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Source: European Commission (2014b: 17-18).

Regarding respondent socio-demographic characteristics, women are slightly more likely than men to agree with the statements that their household is generating too much waste (45% vs. 42%). Age and education are more important factors on the issue of whether the respondent’s own household is generating too much waste (see Table 1). While a majority (51%) of 25-39 year-olds agree with this statement, only 35% of people aged 55 and over do so. The respondent’s level of education is also important to this issue: 48% of people who finished their education aged 20 or over agree that their household is generating too much waste, compared with 35% of people who left school aged 15 or under.

Table 1: Percentage total ‘Agree’ (total of totally and tend to agree) for the following

My country generates too My household generates too

0% 20% 40% 60% 80% 100%

Latvia

Czech Republic

Slovakia

Poland

Estonia

Bulgaria

Hungary

Romania

Italy

Croatia

Germany

United Kingdom

Finland

Malta

Cyprus

Portugal

EU 28

Luxembourg

Lithuania

Greece

Austria

Belgium

Ireland

Sweden

Spain

Slovenia

France

Denmark

Netherlands

My household is generating toomuch waste (total 'agree')

My country as a whole isgenerating too much waste (total'agree')

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much waste much waste

EU 28 87% 43%

Sex

Male 85% 42%

Female 90% 45%

Age

15-24 88% 42%

25-39 87% 51%

40-45 88% 47%

55+ 88% 35%

End of education (age)

Until 15 year of age 85% 35%

16-19 88% 41%

20+ 88% 48%

Still studying 88% 42%

Source: European Commission (2014b:). It is interesting to see whether the scores on the statement that a household generates too much waste, corresponds with the actual amount of waste generated. To analyze this we correlate the waste generated by households per capita (see Figure 18) with the scores on the statement “My household generates too much waste”. The correlation coefficient is 0.46 witch indicate a positive relationship on the country level. So at the country level it seems as if households that generate larger amounts of waste more likely agree with the statement that they generate too much waste (see also Figure 22).

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Figure 22: Correlation between the amount of waste generated by households (2012) and the statement that my household is generating to much waste by country (2014).

Source: European Commission (2014b) and Eurostat (Waste Statistics).

3.2.1.2 Waste management behaviour

Most Europeans practice some form of waste management behaviour. According to the survey ‘Attitudes of European citizens towards the environment’ in 2014 (European Commission, 2014a), 72% of the respondents separated most of their waste for recycling, while 33% reduced waste (e.g. by avoiding over-packaged product and buying products with a longer life). Both percentages are relatively stable over time. In the 2004, 2007 and 2011 surveys, the shares of respondents that separated waste for recycling were roughly twice as high as the shares of respondents that reduced waste.

Waste management behaviour shows large differences across the EU. In several EU member states separating waste for recycling is particularly common. In seven countries, over four-fifth of the respondents did so: Slovenia (92%), Luxembourg (92%), Sweden (86%), Ireland (84%), France (82%), Belgium and Malta (both 81%). On the other hand fewer than half did so in four countries: Bulgaria (23%), Romania (33%), Latvia (39%) and Croatia (49%). Reducing waste is most common among respondents in Germany (52%), Austria (49%) and Belgium (44%), but least common among those in Bulgaria (15%) and Portugal (18%).

0

100

200

300

400

500

600

700

800

20 25 30 35 40 45 50 55 60 65 70

Was

te g

en

era

ter

by

ho

use

ho

lds

in k

g p

er

cap

ita

20

12

My household is generating too much waste (% total 'agree')

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High scores on separating waste for recycling do not always correspond with high scores on waste reduction. For Luxembourg high scores for separating waste for recycling go hand in hand with high scores on waste reduction, but this is not the case for Slovenia and the Czech Republic. This may imply that differences between EU member states can be explained by individuals’ attitudes towards waste, but also that institutional differences about waste management practices between countries are important. Differences in the share of people who reduce waste may also be related to differences in consumption patterns and economic wealth, which is partly reflected in the amount of waste generated by households. However, there is hardly a correlation between the amount of waste generated by households (Figure 18) and the share of respondents that reduce waste (correlation coefficient is 0.09). Also there is no correlation between the amount of waste generated by households and the share of respondents that separate waste for recycling (correlation coefficient is 0.07).

It seems that women are somewhat more likely than man to take environmentally-friendly measures. More woman than men are engaged in reducing waste and separating waste for recycling, although the differences are relatively small (see Table 2). Both age and education are also correlated to the level of waste reduction and separating waste. As respondents become older they are more likely to reduce waste and separate waste, however this trend stops in for the group respondents over the age of 55. Education is also positively related to both the reduction of waste and the separation of waste.

Figure 23: Percentage of respondents that reduce waste and separate most of waste for recycling in 2014 by country.

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Source: European Commission (2014a: 27).

Table 2: Percentage respondents who separate most of their waste and who reduce waste by sex, age and education in 2014.

Separate most of waste for recycling

Reduce waste

EU 28 72% 33%

Sex

Male 70% 30%

Female 74% 36%

Age

15-24 63% 23%

25-39 69% 32%

40-45 75% 38%

55+ 75% 35%

0% 20% 40% 60% 80% 100%

Bulgaria

Czech Republic

Cyprus

Greece

Latvia

Netherlands

Slovakia

Hungary

Sweden

Spain

France

Finland

Ireland

Austria

Germany

Reducing waste

Separate most of waste forrecycling

40

End of education (age)

Until 15 year of age 70% 31%

16-19 72% 34%

20+ 77% 38%

Still studying 65% 22%

3.2.2 WASTE GENERATION BY TOURISM

In their paper on tourist destinations in the Ukrainian Carpathians, Murava and Korobeinykova (2016: 43) observe the urgency to identify the waste problem in order to design a waste management system (WMS) in order “to meet the growing demand of customers for environmentally friendly conditions of recreation”. In many papers, these data are given for the total flow of municipal solid waste but the contribution by tourism is not distinguishable. That quantity can be estimated by multiplying the number of tourist-days in a tourist destination by a figure for the average amount of solid waste that each tourist generates per day.

Not surprisingly, Pirani & Arafat (2014: 322) comment that “there is much variation between hotels when it comes to how much waste per room they are generating on a daily basis. This is because the waste generation rate depends on many variables such as the hotel type, guest attributes, guest and employee activities, and occupancy rate.” This ‘forces’ research into the amount, or percentage of municipal waste generated by tourism into locally specific estimations rather than calculating with the 1 kg per day per tourist. Both Fortuny et al. (2008) and Mateu-Sbert et al. (2013) suggest a method that takes the seasonal fluctuation of tourism into account.3 Fortuny et al. (2008) explain differences in total amounts between calendar months in a tourist destination by the monthly differences in its total population due to number of tourist-days divided by the number of days in that month. These authors themselves only conclude that “a huge amount of the total solid municipal waste generated each year is produced by tourists activities” by comparing the data on the amount generated per person-day in winter and summer in the Balearic Islands in 2004: resp. 1.50 and 2.50 kg (op. cit.: 861). The average figure in 2004 was 1.82 kg.

The conclusion that the contribution of tourism to (municipal) waste generation is both large and increasing is shared by quite a number of authors (e.g. Cummings, 1997; Dileep, 2007; Pirani & Arafat, 2014; Arbulú et al., 2015; Matai, 2015; Murava & Korobeinykova, 2016).

According to Arbulú et al. (2015: 634), “[…] tourism tends to produce more municipal solid waste than other productive activities”. This is related to the growth of tourism as one of the largest industries in the world (Dileep, 2007: 378). Moreover, as an economic sector, tourism has suffered

3 Various studies stress that the seasonal fluctuation in the amount of tourists can have a

large effect on the total amount of waste generation (see for instance Rada, et al., 2014: Ranieri et

al. 2014).

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relatively little, if at all, of the global economic downturn that started in that same year 2007. Referring to the hotel industry only, Matai (2015: 1445) remarks that “it is expected [to] generate[s] huge amounts of waste since it has been identified as the largest consumer of durable and non-durable goods”. Similarly, Zorpas et al. (2015: 1142) observe that hotels “occupy a crucial place in concerns over environmental protection related to tourism and travel”.

Regarding the composition of the waste that is generated by the tourist industry, quite a few various types are discerned from different ‘departments’ - food and beverage (e.g. food, glass), administration (office waste like paper, ink cartridges, printers and computers), and household (cleaning materials, plastic bottles etc.) - and old furniture, bed linen and towels. Most explicit attention in literature is being paid to food waste (e.g. Pirani & Arafat, 2014; Sullivan Sealey & Smith, 2014): a very significant type in the hospitality industry of hotels and restaurants, the branch of tourist industry that produces most of its solid waste. On a high level of generalisation, Pirani and Arafat (2014: 321) comment that food waste “can account for more than 50% of the hospitality waste”. In the UK, about 920,000 tons of food is wasted in the hospitality sector, 75% of which - equivalent to 1.3 billion meals - is avoidable (op. cit. 328). This average of over 50% is composed of approximately 40% from hotels and 60% from restaurants (op. cit.: 334).

3.2.3 WASTE BEHAVIOUR

In 2012 Myung et al. (2012) reviewed environment related research in scholarly journals for the period from 2000 to 2010. In total they found 58 articles of which sixteen deal with consumer behaviour. Twelve of these studies focus on the lodging sector and three on restaurants.4 All these studies examined micro consumer behaviour such as specific belief, knowledge, attitudes and their relationship to behavioural intentions and behaviour. The majority of research focused on measuring environmental awareness or concern to establish a relationship between these measures and environmentally related behaviour such as willingness to pay, use of green products, enhancement of the image of a hotel, and interest in energy conservation and recycling. Myung et al. (2012: 1269) concluded that “these studies often found contradictory results”. They do not go into possible reasons for this.

For the case of ‘willingness to pay’ Kang et al. (2012) give possible explanations. They found several studies that revealed a gap between customers’ perceptions and attitudes towards corporate social responsibility concept (such as green initiatives) and their actual purchasing behaviour, while other studies show that company’s specific socially responsible initiatives seem to positively influence purchasing behaviour. So, consumers’ positive perceptions and attitudes towards environmental issues do not necessarily lead to a willingness to pay for a company’s green initiatives. For instance, if customers identify a company’s motivation as one of increased self-interest (such as profit enhancement and not public service) customers’ willingness to pay for

4 One other study deals with the relationship between environmental attributes and customer

satisfaction.

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green initiatives may turn negative, despite positive perceptions for environmental issues. Also, customers’ willingness to pay for green initiatives vary according to hotel types or segments.

Another possible explanation for the contradictory results between studies that can be derived from the work by Kang et al. is the heterogeneity of the samples of various studies. For example the level of environmental concern may differ between countries which affects not only the consumers’ behaviour, but also the likelihood that hotels will introduce green initiatives. Also cultural differences may play a role, for instance cultures with a higher degree of ‘power distance’ may generate a lower degree of people’s involvement in open discussions and decision-making processes for social responsibility, which consequently leads to less interest in green initiatives in countries with greater power distance.5 The last explanation is that the studies were conducted in different time periods and that concern for environmental issues in the hospitality industry has grown incrementally over time. This implies that more recent studies can have substantially different outcome than ‘older’ studies.

In their research Kang et al. (2012) among U.S. hotel guests found that guests with higher degrees of environmental concerns declare a higher willingness to pay premiums for hotels’ green initiatives. Also, they found that luxury and mid-priced hotel guests are more willing to pay premium for hotels’ green practices than economy hotel quests. Contrary to previous studies, Kang et al. found that male customers showed a greater willingness to pay such a premium than female customers. They ascribe this to different responsibilities for pro-environmental action in households (men are more often responsible for outside practices) in combination with an overrepresentation of men in the survey. Like Kang et al. (2012), Han et al. (2009) also found that a green hotel’s overall image significantly affected the willingness to pay more for these hotels. Although this counts for both women and men, the relationship is stronger for female customers than for male customers.

In the studies by Ranieri et al. (2014) and Rada et al. (2014) interesting claims are made about waste behaviour of tourists. These studies are based on aggregate data on community levels, so their conclusions are not based on data on the level of individual tourists. The case study analysis of Ranieri et al. (2014) in two Italian and one Romanian tourist areas – resp. a Province, a region and a County - records that inefficient behaviour of tourists in selective collection (source separation for recycling) of solid waste contributes to the increase in the amount of residual municipals solid waste. One of the problems the authors observe is that tourists at their tourist destination have to be accustomed to a waste collection generally quite different to their area of original. “A tourist could have a too short time to learn the rules of the collection system before the end of the holiday” (Ranieri et al, 2014: 283). They also assume that mountain tourists are more careful about selective collection compared to other tourists.

Contrary to the study of Ranieri et al. (2014), Rada et al. (2014) found in their study in five communities in the Province of Trento (Italy) that the seasonal variations in the number of tourists

5 Power distance is the extent to which less powerful members of organizations and

institutions accept and expect that power is distributed unequally.

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hardly seem to influence the selective collection efficiency. These authors claim that “the trend of tourists towards shorter periods of holiday does not affect selective collection; possibly because they are used to coming back to the same municipality, where they have already learned the local principles of source separation” (Rada et al. 2014:187).

The studies by Ranieri et al. (2014) and Rada et al. (2014), together with the findings of Kang et al. (2012) suggests that there are different ‘types’ of tourists concerning their waste behaviour, not only by socio-demographic characteristics’, attitudes to green behaviour, but also by country of origin and destination. A study that empirically compares pro-environmental behaviour of tourist at home and at their tourist destination was done by Miller et al. (2015) and looked especially at the poorly understood belief that pro-environmental behaviour weakens when residents become tourists. Either tourists feel more morally obligated in their own communities, or alternatively destinations may not provide an infrastructure that supports environmentally friendly behaviour. After a survey among visitors to Melbourne (Australia), Miller et al. concluded that although paper and plastic recycling were frequently done in both the domestic and tourist context, a recycling drop of 16% was observed which was higher than other pro-environmental behaviours such as green transport use, energy use and green consumption.

Interestingly Miller et al. (2015) found that attitudes are not statistically significant related to recycling in the tourism context, whereas habits of domestic recycling, the availability of recycle bins and the sense of tourist social responsibility are. They suggest that the findings regarding attitudes can be explained by the way the relationship between attitudes and actual behaviour may broke down. “Environmental attitudes may be strongly held, but when exposed to greater challenges and difficulties in a tourism context, they are less likely to be fulfilled in behaviour. Attitudes, per se, are not a strong predictor of urban tourist pro-environmental behaviour”6.

Domestic habits seem to play a slightly lower role in explaining vacation recycling than in domestic recycling. An explanation by Miller et al. . (2015:?)7 is “…that recycling behaviour is institutionalised in the home city, with a convenient,