Towards Quantification of Purchases and Waste Generation at the Level of I ndividual Households
A Pilot-study on Two Swedish households
Waste Generation at the Level of I ndividual Households
A Pilot-study on Two Swedish households
Fen Feng
Fen Feng
Uppsala University, Department of Earth Sciences Master Thesis E, in Sustainable Development, 30 credits
Printed at Department of Earth Sciences,
Master’s Thesis
Towards Quantification of Purchases and Waste Generation at the Level of I ndividual Households
A Pilot-study on Two Swedish households
Fen Feng
2
Content
1. INTRODUCTION ... 5
1.1 RESOURCE CONSUMPTION AND WASTE GENERATION: SIGNIFICANCE OF THE STUDY ... 5
1.2 HOUSEHOLD METABOLISM... 4
1.2.1 Formation of Household Metabolism Concept ... 4
1.2.2 Fluxes within household metabolism ... 5
1.2.3 Household metabolism and Sustainable Development ... 6
1.3 PREVIOUS RELATED STUDIES ON HOUSEHOLD METABOLISM ... 7
1.4 OBJECTIVES OF THE THESIS ... 10
1.5 OUTLINE OF THE THESIS ... 11
2. DATA AND METHODS ... 12
2.1 DATA COLLECTION ... 12
2.1.1 Shopping Receipts ... 13
2.1.2 Waste Analysis: Recyclable Solid Waste... 13
2.1.3 Waste Analysis: Biowaste... 13
2.1.4 Household Characteristics ... 14
2.2 PRODUCT FLOW ESTIMATIONS ... 14
2.3 INDICATORS... 15
2.4 METHODOLOGICAL DELIMITATIONS ... 15
3. PILOT‐STUDY ... 17
3.1 PATTERN OF COMMODITIES CONSUMPTION ... 17
3.2 PATTERN OF SHOPPING FREQUENCY ... 19
3.3 DISTRIBUTION OF PRODUCT MANUFACTURING COUNTRY ... 21
3.4 DISPOSAL OF RECYCLABLES AND BIOWASTE ... 22
3.4.1 Analysis of standardized waste ... 2
3.4.2 Temporal Distribution of standardized package waste disposal ... 26
3.4.3 Analysis of non‐standardized solid waste ... 26
3.4.4 Summary of the proposed method through case study ... 28
3.5 TIME INTENSITY OF DATA COLLECTION... 30
4. DISCUSSION ... 32
4.1 THE IDEOLOGY OF DESIGNING THE PROPOSED METHOD ... 32
4.2 COMPARISON OF DATA COLLECTION METHODS WITH PREVIOUS RELEVANT STUDIES ... 32
4.3POTENTIAL RISKS IN THE FURTHER STUDY BASED ON THE PROPOSED METHOD ... 33
5 CONCLUSION AND RECOMMENDATION ... 35
5.1 CONCLUSIONS ... 35
5.2 RECOMMENDATION ... 35
6. ACKNOWLEDGEMENTS ... 36
7. REFERENCES ... 37
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Towards Quantification of Purchases and Waste Generation at the level of Individual Households: A pilot-study on Two Swedish Households
FEN FENG
Fen, F., 2012: Towards Quantification of Purchases and Waste Generation at the level of Individual Households: A case of two Swedish Households. Master thesis in Sustainable Development at Uppsala University, No. 83, 38 pp, 30 ECTS/hp
Abstract: Continuous increase in resource demand and associated with it environmental impact, by-products and wastes, are going to put strains on the global ecosystem including humans. The thesis was based on the assumption that: The household scale holds important information on flows of resources and statistical relations between them. This master thesis was pilot-study of the project “Quantifying Household Metabolism” which was carried out by Urban Metabolism Research Group (UMRG) at Chalmers University of Technology. The thesis intends to develop methods to quantify fluxes of consumption and waste generation through individual household.
The data was collected in two Swedish households and the collection period in the master project was 2.5 months on solid waste and 1 month on biowaste. The data was collected on a daily basis and from three streams: shopping receipts, recyclable solid waste and biowaste from kitchen. The data gathered by the proposed method illustrated a pattern of consumption and waste generation through individual household based on t h e two studied households. Although the proposed method avoided the errors happened in pervious study methods (Survey), the deficiencies and potential risks exited. The indicators developed to evaluate the situation of household metabolism failed to apply to the testing households in pilot-study. The applied data collection procedure was manual and laborious, and accumulated errors easily happened when it carry out in a long term. Several automatic possibilities could be introduced in the future study.
Key Words: Sustainable Development, Household metabolism, Waste Generation, Goods Consumption, Flows
Fen Feng, Department of Earth Science, Uppsala University, Villavägen 16, SE- 75236 Uppsala, Swede
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Towards Quantification of Purchases and Waste Generation at the level of Individual Households: A pilot-study on Two Swedish Households
FEN FENG
Feng, F., 2012: Towards Quantification of Purchases and Waste Generation at the Level of Individual Households: A case study on Two Swedish Households. Master thesis in Sustainable Development at Uppsala University, No. 83, 38 pp, 30 ECTS/hp
Summary: Continuous increase in resource demand and associated with it environmental impact, by-products and wastes, are going to put strains on the global ecosystem including humans. To achieve sustainable consumption, we have to understand all kinds of possible factors that could affect consumption and waste generation. The thesis was based on the assumption that: The household scale holds important information on flows of resources and statistical relations between them. Yet knowledge on metabolic patterns of individual households intends to inspire novel technologies or policies geared towards decreasing over consuming of natural resources and disposal of waste.
The master thesis study is embedded in an on-going research project carried out by the Urban Metabolism Research Group at Chalmers University of Technology. The UMRG projects aims to quantify spatial and temporal variations in the flows of resources and waste stream on the level of individual households. The main purpose of the thesis study was to develop a method to collect data of commodities consumption and waste generation. The proposed method intends to avoid errors generated by methods implemented in previous household metabolism studies. The data collected by the proposed methods displayed the results in a pilot-study, followed by related analysis and discussion.
The data gathered by the proposed method illustrated a pattern of consumption and waste generation through individual household based on t h e two studied households. In particular, the following issues could be studied:
spatial and temporal information on purchases, expenditure on different categories of products and their geographical origin, generation of different types of waste including avoidable and unavoidable biowaste, product categories contributing the most to the package waste.
Data collecting through the shopping receipts, package wastes and biowaste may not be the optimum plan, but after failing to get the data information through other reliable source such as retail or manufacturers. The proposed method to collect the data seems to be the most appropriate way with high data reliability. The indicators designed to evaluate household metabolism failed to implement in the pilot-study, because of the difference of number of household member in each household. “Package intensity” designed to investigate which products generate most waste didn’t apply to well, due to its complicated ranking standards. Several visible errors occurred, when the proposed method applied to the participated experimental households in pilot-study. Potential risks such as recruitment of voluntary households to participate into the experiment could also be problematic.
Based on the results from pilot-study, data collected by the proposed method could provide a closer insight into household metabolism in two dimensions: pattern of commodities consumption and pattern of waste generation.
The errors generated by previous research methods avoided. However, the proposed methods exited several deficiencies. Due to the manual work of data collection, the data procedure was laborious and time consuming.
For collecting data of recyclable solid waste, several possibilities for automatization exits and information of products is possible to gain from manufacturers or retails. For biowaste, a standard factor could be worked out to convert wet weight directly into dry weight, and then household could just weighted wet weight of biowaste at home and provide the wet weight of biowaste.
Keywords: Sustainable Development, Household metabolism, Waste Generation, Goods Consumption, Flows
Fen Feng, Department of Earth Sciences, Uppsala University, Villavägen 16, SE- 752 36 Uppsala, Sweden
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1. Introduction
1.1 Resource consumption and waste generation: significance of the study
It is estimated by UNDP (United Nation Environmental Programme) that 140 billion tons of minerals, ores, fossil fuels and biomass are going to be consumed annually by 2050. This number is three times higher than its current consumption volume (UNDP, 2011). When viewed from another perspective, world population is estimated to increase by 47 percent from 2000 to 2050 (UNDESA, 2004). This data reveals not only a conceivable storage of resources but also a potential threat to the Earth’s biocapacity. Although world population growth rate decreased from 2.069 percent in 1965 to 1.162 percent in 2010, the population quota is large while many resources are non-renewable and limited (UNDESA, 2011).
Another issue that has to be emphasized is waste, which is discarded or abandoned and continuously burdens the environment. To some extent, waste could be considered as by-product of consumption. Although consumption is not always accompanied by waste generation, more consumption could increase the rate of waste generation. Therefore, understanding household consumption patterns may potentially contribute to form strategies to decouple consumption from depletion of natural capital and to alleviate some of the related environmental impacts.
Since sustainable consumption was initially discussed in Rio de Janeiro at the UN Conference on Environment and Development in 1992, it has attracted more and more researchers to study it (UNDP, 2012). The 2012 annual
student conference organized by BUP (Baltic University Programme) held in Rogow, Poland also puts their concentration on sustainable consumption. The presented information sent out a signal that continuously raising resource demand and its associated by-products and waste, are going to put strains on people and our planet.
To achieve sustainable consumption, we have to understand all kinds of possible factors that could affect consumption, just as a doctor has to diagnose the symptoms of a patient before delivering the prescription. In general, metabolic rate is intuitively connected to GDP (Gross Domestic Product), which usually represents a direct proportion relation. Currently, GDP is the most common indicator to measure economic performance throughout the world. According to the definition by the European Parliament, GDP represents the market value of all final goods and services produced within a geographical entity within a given period of time (Schepelmann et al, 2010). As it can be seen from Fig.1, most of the African countries converged in the area of low GDP and low metabolic rate, while more industrialized countries occupied the area if high GDP and comparatively high metabolic rate. This distribution implies that a positive linear relationship exists between metabolic rate and income level. One irregular relationship is worth nothing, namely that for some of the countries the metabolic rates were far apart, even though the per capita GDP was almost identical. For example, it can be seen from Fig.1 that per capita GDP of New Zealand was nearly the same as for Puerto Rico, whereas Puerto Rico’s metabolic rate was much lower compared with New Zealand’s. Guyana and Congo could be another extreme example where two countries had similar per capita GDP but a great deal of difference between their metabolic rates. This suggests that there must be some other factors
2 than GDP affecting the metabolic rate. As Jared Diamond (2008) indicated, the consumption rate is not closely coupled with quality of living. The article pointed out that per capita oil consumption of Western Europe was only half of the number of USA, while Western Europe held higher living standards as judged by life expectancy, health, infant mortality, medical care and pension etc. Hence, we assumed that there must be some other factors that could affect the metabolic rate through the whole society from the perspective of culture. In other words, consumers’ lifestyles could be a crucial factor driving consumption, resource and waste flows.
In 2011, World Economic Forum in collaboration with Accenture carried out a program to see how environmental impacts could be reduced through shrinking consumption.
In this study, a survey investigating how consumer attitudes and behaviors affect resource consumption and environment has been delivered by Ageis Media to 10,000 people throughout 40 countries. The results displayed that 66 % of consumers value the environmental benefits when considering their consumption choices. Yet none of the consumers considered environmental impacts or resource shortage when it came to choices regarding their electricity bill, which means that none of the participants in the study was willing to pay extra charges for environmental concern (World Economic Forum & Accenture, 2012). For those participants, price was the first concern when they were consuming. However, socio-cultural aspects might have a strong influence on changing consumers’ lifestyle. For example, due to consumers’ concern of food safety, organic food, because of its associating with merits of personal health, animal welfare, and environmental protection, greatly became people’s preference choice (Magkos et al, 2006).
According to FAO (1999) and Greene (2000), it
was until now that organically growing food became one of the fast developed agriculture in US and Europe, which was also quickly expanded in other areas of the world (Willer and Yussefi, 2004). It seems that the concern of health-related issues is the most crucial reason that leads to the great increasing of organic food because of its safety (Magkos et al, 2006).
However, the perception of safety of organic food chiefly attributes to its organic food production (Magkos et al, 2006). According to the United Kingdom Parliament (1999), Soil Association (1997) and Codex Alimentarius Commission (2001), organically growing food does not use synthetic fertilizers pesticides, growth regulators, and livestock feed additives.
Supports from scientific researches confirm this perception that organic food is healthier and safer than conventional food (Magkos et al, 2006). The highly growing demand of organic food may mainly ascribe to its widespread belief of its organic production. Therefore, we assume that consumers’ lifestyle should be associated with social-cultural perspective.
Consumption patterns vary greatly from person to person and depend on consumers’ income, personal characteristic, habits and lifestyle etc.
This heterogeneity, however, is lost in larger spatial scales. Still, we assume that some consuming similarities could be found among the households with similar characteristics such as the number of household member, family income, house type etc. Whereas households are considered as small units that make family members connected for interacting with each other or household chores, seen from political and scientific viewpoints, household acts as an important social-economic sector. Raw materials or resources that are transferred into service and goods, such as water, energy, food etc., by various infrastructure configurations are mostly consumed by residents (Wolman, 1965). The following research assumption is hence
3 formulated: The household scale holds important information on flows of resources (attributes) and statistical relations between them.
Information on variations among individual private households is lost through aggregating to the municipal scale. Yet knowledge on
metabolic patterns of individual households could contribute to inspire novel technologies or policies geared towards decreasing overconsumption of natural resources and disposal of waste, which could be better than the traditional environmental protection method,
Fig. 1 . Global Interrelation between resources use and income Source: UNDP. Adapted source: Decoupling natural resources use and environmental impacts from economic growth, p14
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“end-of-pipe”.
1.2 Household Metabolism
1.2.1 Formation of Household Metabolism Concept
According to the dictionary (2012), the definition of metabolism is a series of chemical processes within a living cell or organism that are necessary for maintenance of life. In this context, metabolism encompasses various kinds of chemical reactions. All function as ‘engine’
driving the life cycle. For example, cells generate substances and energy to sustain physical life, while organic compounds are broken down to provide heat and energy to the body, as well as simpler molecules are also used to build more complex compounds like proteins for growth and repair of tissues.
The initial appearance of the concept of
‘metabolism’ was surely formed to describe the chemical processes to maintain life in a living cell or organism. However, usage of the term
‘metabolism’ gradually extends to other processes and contexts. The metaphors of other systems could range from natural resources consumption within the ecosystems to activity
changes within social systems (Brunner &
Rechberger, p30, 2005).
The introduction of ‘metabolism’ in the context of human activities intends to display the relationship between human activities and the related consumption of materials and energy associated with the respective impacts on the environment. Metabolism encompasses consuming resources and generating wastes within a loop system, which could be viewed as an organism. Nevertheless, metabolism within an organism is usually determined by several internal factors. That is to say the state of the metabolism ought to relate to the characteristics of the organism. Different function, structure and structure and status etc. of an organism incorporate various metabolism states.
‘Anthrosphere’ as a boundary to describe the human sphere of life has been formed, which is considered as part of the ecosystem, because all of its processes happen in the boundary of the
‘anthrosphere’ and also interact with it. The anthrosphere is viewed as a living organism, while materials, energy and other matters could be considered as cells, organic compounds or molecules to sustain the human life and activities, see Fig.2 (Brunner & Rechberger, p49, 2005).
Metabolism within the anthrosphere could be
Fig. 2. A region where anthropogenic activity takes place can be divided into the subsystems
"anthrosphere" and "environment". Exchange of materials takes place within the subsystems as well as between the subsystems and processes outside the region Source: Brunner & Rechberger, p23, 2005
5 linked to four main fields: agricultural industries, trade and commerce; private households; and waste management. All of these four compartments directly and indirectly interact with human activities (Brunner & Rechberger, 2005). Due to an ambiguous boundary between anthrosphere and environment, the definitions of anthrosphere are various. Aforementioned, anthropogenic activities drive the flow of resources and matter. In this thesis project, the concept of metabolism specifically refers to consumption of commodities and generation of waste by individual private households.
The sharp increase of the number of household and a slowly growing population are a tendency in many European economies (Noorman &
Uiterkamp, p3, 1998). There is 50.5% of the total population living in cities with 1.85%
urbanization rate (WDP, 2012). It implied that households as an anthrosphere system own a worthy researching boundary.
Consumption of natural resources and disposal of waste at the level of individual households are referred as household metabolism, and earn more and more attention by environmentalists, economists and social scientists. Notice that household activities consumed about 28% of US energy in a direct way, 57% of total economy activities are related to household, and 15% is
for non-household related consumption activities (Bin & Dowlatabadi, p3, 2005). The researching
of household metabolism to track the footprint on natural capital is getting more and more attention among environmental scientists.
The general concept of “household metabolism”
includes direct and indirect resources usage. The direct resource usage refers to the demand for resources through households, such as heating and motor fuel, while indirect resource consumption is associated with the resources required to produce the products and services are finally consumed by that households. The indirect resource consumption related to
“industrial metabolism”, such as mining, house construction, waste treatment etc., and finally served consumers. Industrial metabolism refers to the flows of natural resources entering into the production and supply chains, as well as waste generated.
1.2.2 Fluxes within household metabolism
Fig.3 presents the physical fluxes entering or leaving the household. According to the figure, the primary inputs are food, water, electricity, fuel, durable goods and heat, while outputs are sorted as wastewater, solid waste, biowaste, residual waste, residual heat and durable goods waste. Durable goods here refer to products that can be used for a long time such as household appliances. In this thesis, environmental impacts
Fig. 3. Input and output fluxes through an individual household
6 generated by households and released to air or soils are not within the research boundary.
The current thesis places focus on examining two dimensions: metabolic rate of commodities consumption (mainly food consumption) and solid waste generation (mainly recyclable solid waste and biowaste) at the level of individual households.
Solid waste is an output which is disposed of by households. Terms describing solid waste that appear in newspapers or articles are various, such as “waste material”, “trash”, “disposals”,
“rubbish”, or “ garbage”. Solid waste is any discarded matters which can be semi-solid or containerized gaseous material, while Municipal Solid Wastes (MSW) refers to used products we throw away in everyday life, such as food waste, furniture, newspapers, products packages, batteries etc. (USEPA, 2010). In the current thesis, solid waste refers to solid waste generated by household.
1.2.3 Household metabolism and Sustainable Development
The concept of sustainable development originated from concerns about environmental degradation in the 1960s. Due to continuous economic growth, high consumption and waste generation, the environmental load becomes more and heavier along with a gradual shortcoming of natural capitals (Noorman &
Uiterkamp, p10, 1998).
The general accepted definition of sustainable development was initially proposed in the World Commission on Environment and Development held by the United Nations in 1987 as
“Brundtland Report” which was embedded in the conference report “Our Common Future”
(WCED, 1987). “Development that meets the needs of the present without compromising the
ability of the future generations to meet their own needs” was the core idea of the Brundtland Report (WCED, 1987).
Although many narratives about sustainability were already proposed by environmentalists, social scientists and economists, the idea of sustainable development was formally transferred into protocol in the conference held by the United Nations on Environment and Sustainability at Rio de Janeiro in 1992 (UNCED). Most of the countries participated in this conference (UNCED, 1992). The protocol generally known as “Agenda 21” had been formed to present the details for countries to make environmental policies as their objectives, which was supposed to act as the monitor to make sure that countries could grow under the concept of sustainability. The commission on Sustainable Development thereby was established to promote development under sustainability with its regular report, which is also known as Earth Summit (CSD).
Although the idea of sustainable development has been widely accepted since 1987, it was difficult to practically make it achieved. When the meaning of ‘sustainable’ and ‘development’
literally inquired, it is not difficult to apprehend why it is so hard to implement ‘sustainable development’. ‘Sustainable’ is widely recognized as to maintain in a long term, while
‘development’ is more viewed as ‘growth’.
However, a contradiction exists between saving the natural capital and reducing the environmental impacts and economic growth (Noorman & Uitkamp, p15, 1998). Economic growth means more business trade should happen, which indirectly implies that more resources will be consumed to develop the economy. Furthermore, lack of the normative to sustainable development is also an obstacle to make it a practical issue.
7 The paradigms of ‘strong sustainability’ and
‘weak sustainability’ are commonly discussed in academic fields, whereas these two standards overlap. The criteria distinguishing ‘weak sustainability’ and ‘strong sustainability’ are gradual steps from emphasis on an anthropogenic perspective towards emphasis on an ecocentric perspective; the definition of
‘carrying capacity’ could be considered as the extreme ‘strong sustainability (Noorman &
Uiterkamp, p15, 1998). Whatever the definition of sustainability and the balance between anthropogenic perspective and ecocentric perspective are, it always comes down to a
‘dialogue of compromise’ between human livability and natural environment. The discussions about sustainable development thereby always surround three crucial pillars:
social, economic and environmental perspectives, which include most of human activities and their biophysical interactions with ecosystems (Noorman & Uiterkamp, p20, 1998).
As mentioned above, environmental quality has already received some attention. Millions of people and jobs engage in environmental protection. Wastewater treatment technologies, recycling and reuse of solid wastes as well as other environmental pollution treatment are on the way to be improved. Nevertheless, besides
recycling and reusing wastes to reduce the consumption of raw materials and energies, rare concerns has been given to approach the sustainability in the perspective of a social element. It is always the theme of co-existence between human and nature. As Ayres and Kneese estimated, today roughly 90% of the human consumption relies on fossil fuel (Noorman & Uiterkamp, p22, 1998). On that account, we assume that cutting down the consumption of natural resources could be based on understanding the consumers’ lifestyles. The appropriate solutions or policies could be formed out to tackle the tough resources shortcoming, as well as abating simultaneously its relative environmental problems.
1.3 Previous related studies on household metabolism
There is abundant literature investigating metabolism in the context of consumption.
However, the scope of most of studies was on a regional or city scale (Kennedy et al., 2007).
Few studies have investigated the metabolism of individual households. Based on the topic of household metabolism, a brief introduction of evolution of metabolism is described to give readers the illustration of the researches related
Fig. 4. Extended metabolism model Source: Adapted from Newman, 1999.
8 to household metabolism.
To narrate household metabolism, a general history of metabolism has to be introduced.
Studies on metabolism were initially carried out by Abel Wolman in 1965 in his essay “The metabolism of cities” and largely done in the early 1970s (Duvigneaud and Denayeyer-De Smet 1975; Hanya and Ambe 1976; Newcombe et al. 1978, 2008 cited in Codoban &Kennedy).
Later on, indicators of human livability were added into the term of urban metabolism to extend the definition of sustainability by Newman in a similar study as Fig.4 illustrated (1999).
Although many studies have been carried out to measure metabolism, most of them focused on cities as a unit of investigation. Presently, the importance of households as a unit of society and a driver for resource consumption is increasingly being recognized. Tab.1 presents two most relevant household metabolism studies with their research methods, studied resource types, data sources, the number of studied households and main conclusion.
The first study was embedded in ToolSust project. The project examined metabolism situation of households in Södermalm, which is one of highest densely populated areas in Stockholm. The ToolSust project quantified household metabolism by calculating total energy requirement through household consumption. Total energy requirement contained both direct (energy consumed directly in the household) and indirect (energy consumed during the industrial processing, transportation, service) energy consumption related to human activities. This project extracted the data from economic input-output data and energy statistics, and then calculated the energy intensity of industrial processes by the methods of energy analysis technique. Then, energy requirements of
goods and services were evaluated based on the energy intensities of production sectors in conjunction with data from household expenditure surveys. The indicators included flows of energy and materials through households, and the production of waste, as well as use of appliances such as refrigerators, washing machines and the use of the car etc.
(Noorman & Uiterkamp, 1998). Besides Sweden, household metabolism in the Netherlands, UK, Italy and Norway has also been studied simultaneously (Kanyama & Karlsson, 2002;
Moll, & Noorman, 2002). There were two conclusions from this study:1)it’s possible to portray household total energy use; 2)individual households deviate substantially from an average pattern (Kanyama & Karlsson, 2002; Moll, &
Noorman, 2002).
In general, energy use has received more attention than materials use in the studies of household metabolism. However, Household Flux Calculator (HFC), study 2, was established to quantify the flown and nutrients such as carbon, nitrogen and phosphorous etc. through individual households (Baker et al., 2006). HFC calculated element fluxes in suburban households and there were 35 households in Minneapolis--St. Paul metropolitan area in USA participating in this pilot study. Data sources were versatile, which came from 1) questionnaires 2) energy bills 3) odometer readings from households’ cars 4) measuring lawn, garden areas and the number and dimensions of trees in the area 5) public statistic and information. Indicators to quantify the household metabolism have been gradually extended comprehensively to analyze the amount of materials consumed and the waste flows. The conclusion of this study was that high consuming households could greatly cut down their C and N fluxes without compromising their current quality of life (Baker et al. 2006).
9 Tab.1. Previous studies of Household Metabolism
Previous studies to measure household metabolism relied predominantly on the data from public statistics (Fel! Hittar inte referenskälla.). Public statistics are usually collected through surveys, a method that has its limitations as discussed below which may impact the accuracy and the reliability of data. There are two main limitations in surveys, sampling error and non-sampling error:
Sampling Error: The error could be potentially generated by survey respondents/samples, participation and such.
Non-sampling Error: The errors are caused by flaws of questionnaires itself.
Sampling Errors are usually caused by survey respondents. For instance, when doing a survey, especially those containing many questions, respondents might lose the attention to answer them cautiously. Another example includes misunderstanding of the questions, particularly when questions are ambiguous. National Surveys are usually standardized, which may lead to lack of certain specific pertinence to the targeted scientific study questions. Furthermore, the flaws occur because the answers to the questionnaires are estimated by respondents. Obviously, such estimations are prone to be subjective judgments and lack objective principles, which is particularly evident in Household Expenditure Surveys. For instance, questions like “which fuel is used most for cooking?” potentially introduce a subjective answer not based on objective data. To
No. Authors Methods Resource
type
Data source Household number
Main conclusion 1 Kanyama,
C.A. &
Karlsson, R, 2002.
Economic Input-Output Energy Analysis
& Process Analysis
EAP database
Direct and indirect energy demands by household
Household budget expenditure surveys &
goods and services price information
All residents in Södermalm region (Stockholm, Sweden)
1.It is possible to portray household
total energy use (both direct and indirect)
2. households deviate
substantially
from an average
pattern 2 Baker,
A.L. et al, 2006.
HFC(Household Flux Calculator)
Fluxes of C,N,P
Survey, odometer, energy bill, measurement of areas &
public statistic
35 households in
Minneapolis-St.
Paul metropolitan area(USA
)
Sustainably C and N could be reduced in high
consumption household without compromising their current lifestyle
10 map the specific resource use and waste generation by individual households, it is important to have specific objective data to analyze. Another issue that needs to be pointed out is that the data from Household Expenditure Surveys is using price as the quantitative criteria, which could lead to the error when transferring the unit of price into another unit of measure. For example, the price of commercial commodities fluctuate frequently, therefore the transfer from price to kilogram of products could possibly generate a large margin of error.
On that account, the master thesis intends to develop a method to avoid these errors generated by surveys.
1.4 Objectives of the Thesis
The master thesis study is embedded in an on-going research project carried out by the Urban Metabolism Research Group (UMGP) at Chalmers University of Technology. The UMRG project aims to quantify spatial and temporal variations in the flows of resources and waste streams on the level of individual households, while this thesis lays its focus on exploring an appropriate way to collect data of commodities consumption and waste generation on the level of individual households. I came across this project through Nationella Exjobb-poolen, which is a Swedish website for students to search for proper thesis project via it. I thought the topic of the current project would be interesting, and then I sent email to the project contact person Robin Harder who was my thesis supervisor later. After more understanding of the meaning of project, I decided to join the study group and did my master thesis project related to topic of household metabolism. Because the project was pilot-study, only two colleagues in department of Civil and Environmental Engineering pioneered as volunteers to participate into this experiment.
There will be no direct comparison between the two households participating in as they are inherently different. One household is a large family with two adults and five teenage children, while another is a one-person household. On this account, the results are expected to show both varieties and similarities, which might contribute to designing and planning further studies. The expected results should then be transferred into the recommendations to the further expanding research.
A database is expected to be constructed to store all the data collected by the proposed method.
The database is supposed to reflect patterns of household metabolism in the perspectives of products consuming rate associated with waste production in each of the pilot households relating to their household characteristics, such as income, number of household members, housing type, house location etc. Various households essentially have different living and consumption habits, but similarities can also be generalized as several household models present different household types in certain geography and climate regions.
It is prominent that in previous studies indirect assessment methods are used, such as estimation through input-output tables and expenditure surveys. In addition, aggregated data on a national or city level are used available from public statistical sources. However, it is uncertain whether important knowledge is lost through the use of the indirect methods and data aggregation especially as some studies have shown considerable deviations from the average patterns (Carlsson-Kanyama et. Al, 2002). Therefore, the current thesis develops a direct method for quantifying fluxes of consuming goods and generating waste through individual household.
The specific objectives are the following:
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1. Develop a method for quantifying household’s resource flows through products.
2. Discuss reasonable requirements for the method to obtain accurate results.
3. Investigate how the method can be applied to future studies appropriately.
4. Based on data collected by the proposed method, a general analysis will be presented to understand scenarios of household metabolism in the aspects of commodities consumption and waste generation, pioneering as pilot-study.
Meanwhile, methods to quantify other physical fluxes such as electricity, heating, water have been carried out by other group members.
1.5 Outline of the Thesis
The thesis is deployed as the following construction layers: Introduction, data and methods, pilot-study, discussion, conclusion and recommendations, acknowledgements and references.
Introduction indicates the significance of the study and illustrates the conceptual theory of household metabolism. After this, the previous studies review and current situation are also presented in this section. Following the former studies reviewing, the terms of each attributes towards household metabolism will be defined.
The study objectives are also included in the first section. The data collection methods and research methodologies are elaborated in the second chapter. Methodological delimitations would be elaborated in the chapter two. Chapter three presented the pilot-study, which mainly displayed the data collected by the proposed method. At the mean time, analysis and discussion also presented around the collected data from two experimental households. Chapter four was discussion around the proposed method, and then followed by conclusion and recommendations. Chapter seven was acknowledgement to thank to the people who provide help to the completion of this thesis. The framework of thesis is illustrated in Fig.5.
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2. Data and methods
The research of this thesis project mainly consists of three elements: literature review, data collection and evaluation of the methodology developed. In this chapter, data collection is described more in detail.
The whole study is composed of three stages. The first stage consisted of literature review and conceptual model formation of household metabolism, while database building was carried out simultaneously. The literature review covered former relevant studies and research.
The second stage laid emphasis on data collection of waste generation and goods consumption by two test households. Data collection comprised three parts: product package waste data, daily shopping receipts data and biowaste data.
The third stage, thesis writing, focused on data extraction and analysis. Followed by discussion and conclusion, some personal recommendations for further expanding the study shall be given and is also presented in the thesis.
2.1 Data Collection
The thesis study mainly undertakes the part of developing a method to collect solid waste analysis and commodities consumption. Fig.6 illustrates the input of goods and output of waste to and from an individual household, which will be measured through the proposed method in this thesis. Inputs include
products associated with their packages, while outputs are made up of recyclable solid waste, Biowaste, bulky waste, dangerous waste and residual waste.
After utilized or consumed, input products are finally transferred or abandoned as output waste, while input packages of products are direct waste. Recyclable solid waste refers to solid waste that could be reused or recycled. In the current thesis, the proposed method laid on it emphasis on collecting data of recyclable solid waste and biowaste. Recyclable solid waste is sorted into two categories: standardized waste and non-standardized waste. The term of standardized waste is package waste with identified barcode.
Non-standardized waste refers to solid waste without barcode, which probably could be newspaper, paper, metal or packages without identified barcode.
In this project, solid waste is considered as one of the indicators to evaluate household metabolism.
Standardized waste, non-standardized and biowaste will be examined separately in each type of waste material.
According to the European Commission (2012), biodegradable garden and park waste, food and kitchen waste generated by households, restaurants, caterers and retail premises, and comparable waste from food processing plants are all classified as biowaste. In this study, biowaste refers to food and kitchen waste produced by households.
Harzoudous waste mainly refers to the waste that could threaten public health or environment. It could
Fig. 6. Input (products and package) and output (Waste)
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Be generated by many industries as well as households and can appear in the form of liquid, solid and gas such as disposal pesticide etc. (USEPA, 2012). Meanwhile, electronics such as computer, televisions, cell phones are considered as harzoudous waste generated by household, which potentially endanger public environment and health.
Currently, there is no standardized definition to the bulky waste from authoritative organizations. The definition in this study is introduced from BulkyWaste.org which defines bulkywaste as unwanted household items that will not fit into normal size rubbish bin, such as furniture, washing machines, carpets, etc. (BulkyWaste, 2012).
The definitions to residual waste are not unified.
However, the most popular one refers to the waste material not recyclable, reusable or compostable.
The source of data in this thesis study comes from the following quantities:
1. Shopping receipts
2. Waste analysis (i.e., standardized waste, non-standardized waste and biowaste)
2.1.1 Shopping Receipts
Shopping receipts provide the amount of shopping items, weight of some products’ and price of each item as Tab.2 shows. Meanwhile, each of the shopping receipts indicates a specified shopping data, store address, organization number, as well as price of each item with its discounts. The total price of each shopping event is also recorded. Shopping receipts are considered as the main reference of the commodities flowing into the households, which is based on the assumption that every product is on the list of shopping receipts. Shopping receipts should be collected by the households for analysis.
Tab. 2 Information of items on shopping receipt with units
2.1.2 Waste Analysis: Recyclable Solid Waste
Recyclable Solid Waste should be collected by the households every day (Biowaste should be collected and processed separately). Packages wastes from recyclable solid waste provide information in two ways. First, the data of material type and quantity of each product’s package are entered into the database as output waste. There are two types of solid waste contained in the package analyses: standardized waste and non-standardized waste. Both standardized waste
and non-standardized waste are categorized into seven waste fractions according to their material type, which are wood, metal, glass, paper, soft plastic, hard plastic, and cardboard. Tab.3 displays the spreadsheet of weight of standardized waste and non-standardized waste.
Tab.3. Weight of waste in package analysis Product Barcode Barcode
Glass (kg) Metal (kg) Soft Plastic(kg) Hard Plastic (kg) Paper (kg)
Cardboard (kg) Wood (kg)
Second, the information contained on the packages will supply detailed information of each product including weight of product, product name, manufacturer name, manufacturing country, and barcode. A spreadsheet embedded in the database presents the information that package waste provides, see Tab.4.
Tab.4. Products information extracted from package of the waste
Product Name
Manufacturer Product Weight (kg)
Manufacturing Country
Barcode
2.1.3 Waste Analysis: Biowaste
Biowaste was collected from the household every day.
Biowaste needs to be sorted into four fractions by experimental households: (1) vegetable waste and peelings, (2) fruit waste and peelings, (3) wasted food and (4) other biowaste.
Waste food meant that food had been cooked and was left for a long period, some of which were probably avoidable to be waste. These four types of biowaste were sent to the oven dried for certain hours depending on its amount. Meanwhile, all the Biowaste was weighted before drying and after drying, and the weights shall be recorded in the database. Tab.5 shows the spreadsheet of biowaste data collection.
Shopping Nr. ShoppingStore Nr. of Items ProductPrice (SEK) Product Weight (kg) Shopping Date
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Tab. 5. Biowaste data collection category with units Sample_id
Veg Waste&Peel (kg)
Wet Dried
Fru Waste&Peels (kg)
Wet Dried Wasted Food(kg) Wet
Dried Other Biowaste
(kg)
Wet Dried
All the data should be entered into the database.
2.1.4 Household Characteristics
Household members consume commodities and generate waste, while consumers’ capability and volume are related largely to their age. In other words, the constitution of numbers and characteristics of household members is one of the leading factors driving the household metabolism. The survey like Tab.6 including information indices and containing general information of each individual household thereby shall be delivered to household to collect the data on household characteristics.
Tab. 6 Household information indices HH-ID HH-Name House Type
Family Income Age < 5
Age 6-11 Age 12-19 Age 20-39 Age 40-69 Age > 60
2.2 Product Flow Estimations
Data obtained from shopping receipts and package analyses are complementary. However, it is often difficult to relate a certain shopping receipt item to a certain package item. This is because the names of items on shopping receipts are often not identical with the names on the product packaging. Furthermore, different supermarkets use different names on the shopping receipt to refer to one and the same product.
Furthermore, shopping receipts and packages are easy to lose by households. As stated above, shopping
receipts only provided the mass of a very limited number of products, while package analysis also lacked some of the weight information on package waste. Due to this reason, it was not sufficient to rely on one of the data sources only. On that account, three different methods have been developed to estimate the mass of products.
Product flows based on shopping receipts
First, product flows were estimated based on shopping receipts. Two methods to estimate value of products value were embedded in shopping receipt. One was estimated according to price of each item on the shopping receipts.
Another method was estimated according to products with data of weight that were obtained from shopping receipts.
is the estimated weight of the products on the shopping receipts. is the total weight of the products on the shopping receipts that indicate a weight. is the total amount of products on the shopping receipts that indicate a weight. is the total price of the products on the shopping receipts that indicate a weight. P was the total price of products on the shopping receipts. is the total amount of products on the shopping receipts.
Product flows based on waste analysis
Second, product flows were estimated based on waste analysis.
is the total weight of the products based on disposed packages with product weight indication.
is the total amount of disposed packages with product weight indication. is the total amount of disposed packages.
Product flows based on both sources
Third, product flows was estimated according to product flows consistency. The formulation below had
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been formed out to calculate the estimating mass value:
is the total product weight that was estimated by our calculation according to the formulation above. is the total product weight of products with product weight indication on the shopping receipts. is the total product weight of products with product weight indication on the disposed packages. was the weight of the products that could be gathered from both shopping
receipts and package analysis (and hence are counted double).
2.3 Indicators
Moreover, indicators to examine the goods consumption and waste generation have been formed.
It includes the following three categories: 1) Intensity of products consumption 2) Intensity of Waste Generation, and 3) Spatial and Global Diversity of products manufactured. Please see Tab.7 below:
Tab.7. Indictors for Household metabolism with units Intensity of products consumption
Food consumption
Solid Food Consumption (Kg Per Household) Liquid Food Consumption (Liters Per Household) Non-food consumption
Intensity of Waste Generated
Per household Biowaste Generated
Avoidable Biowaste Generated (Kg Per Household) Unavoidable Biowaste Generated (Kg Per Household) Per household Solid Waste Generated
Wood Waste Generated (Kg Per Household) Glass Waste Generated (Kg Per Household) Paper Waste Generated (Kg Per Household) Metal Waste Generated (Kg Per Household) Soft Plastic Waste Generated (Kg Per Household) Hard Plastic Waste Generated (Kg Per Household) Cardboard Waste Generated (Kg Per Household) Unidentified Waste Generated (Kg Per Household)
Spatial and Global Diversity Ratio
Ratio of products Manufactured in Europe (Kg Per Household) Ratio of products Manufactured in Asia (Kg Per Household) Ratio of products Manufactured in America (Kg Per Household)) Ratio of products Manufactured in Africa (Kg Per Household) Ratio of products Manufactured in Australia (Kg Per Household)
2.4 Methodological delimitations
There are diverse limitations to the proposed methods applying to the pilot-study. The limitations listed below were easily visible.
First of all, two experimental households selected were without random. The results and similarities of pilot-study cases existed bias. The testing households chosen were also without considering the wealth
disparities, which also affected the similarities and differences between two households.
Secondly, the amount of data collection period to the testing households was 2.5 months for the wastes packages and 1 month for the biowaste. On that account, there were no durable goods such as furniture, refrigerator etc. abandoned as waste in the database, which also has an influence on the reliability of the results. Predictably, there must be
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durable goods discarded during a long term experimental data collection.
Thirdly, due to data collecting manually, errors could easily happen during the data processing and entering period. At the meantime, accumulated errors could occur for the initial data entering. For example, once one type of milk box was entered in a wrong data, the error would accumulate. Because one type of products package would only be weighted once and then entered into the database, and then the rest of package of this item would only be indexed according to the item barcode.
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3. Pilot-study
Household metabolism may vary for different countries, cities, regions, and also from household to household. However, some similarities of consumption and waste generation of household can still be induced through comparative analysis. Tab.8Fel! Hittar inte referenskälla. and Tab.9 respectively describe household characteristics of HH-1 and HH-2.
HH-1 almost cooked at home every day and frequently hosted parties on weekends. On that account, the case study was prone to present data collected though the proposed method we developed and to explore some similarities on household consumption and waste production patterns, for example, the day of week with the most waste generation or the highest overall number of shopping events. In order to illustrate how consumption and waste generation from households may impact the environment, some necessary contrasts will be presented to initiate the debate.
Tab.8. Household characteristics (HH-1)
HH-ID 1 HH-Name GM
House Type Villa
Family Income Unspecified
Age < 5 0
Age 6-11 3
Age 12-19 2
Age 20-39 0
Age 40-69 2
Age > 60 0
Pet 1
Tab.9. Household characteristics (HH-2)
HH-ID 2 HH-Name RH House Type Apartment Family Income Unspecified
Age < 5 0
Age 6-11 0
Age 12-19 0
Age 20-39 1
Age 40-69 0
Age > 60 0
Pet 0
Due to the low number of participating households, the results cannot be used to infer universal patterns of consumption and waste generation, but are rather used to illustrate possibilities of data collection at this level of detail and indications for some observed patterns.
3.1 Pattern of Commodities consumption
In this study, there are two ways to estimate the consumption of goods: disposed product packaging and shopping receipts. Currently, the difficulties in combining these two types of data sources leads to a large uncertainty in the product flows estimated. On that account, only shopping receipts are considered as the data source to analyze the consumption of goods of two households. Shopping receipts mainly provide the number and price of the products, while information on product weight is only available for a very limited number of products. Hence, the general analysis to understand consumption of goods was accorded to the number of products and price.
18 According to Fel! Hittar inte referenskälla. and Fel! Hittar inte referenskälla., HH-1 spent 87%
expenditure on food and 13% on non-food products, While HH-2 cost 76% on food and 24%
on non-food products. The expenditure here referred to money was only spent on consumer goods (including fast-moving products and durable goods). The results displayed that food products occupied most of household expenditure on consumer goods. However, there was one constraint that could potentially affect the results.
The occupation non-food products purchasing frequency is low, which usually could be used for a long time, mainly referring to durable goods like household appliance such as refrigerator,
oven, stove etc. A longer study period could
enable to account even for the durable goods.
When using the amount of products to analyze the consumption distribution, there was no large difference between the two households. Around 15% was non-food products and 85% was food.
Fig.9 and Fig.10 respectively illustrate the food items distribution of HH-1 and HH-2. According to Fig.9, around 24% of expenditure was spent on purchasing meat by HH-1, while 15% on Alcohols, 13% on Dairy products 9% on vegetables. When using the number of products as standard, about 17% of vegetables were consumed, while 17% was dairy products and 11% was meat. Fig.10 showed the food Fig.7. Consumption of food and non-food commodities according to price of
products
Fig.8. Consumption of food and non-food commodities according to price of products
19 distribution of HH-2. 17% of expenditure was spent on vegetables, while 15% on dairy products and around 12% on meat and non-alcohol beverage. When using products’ amount as
standards, 24% of items consumed was vegetable, while 18% was fruit and 12% was dairy products.
3.2 Pattern of shopping frequency
The data sources for shopping frequency were each household’s shopping receipts: each receipt
was accounted for as one shopping event. Fig.11 shows the accumulated shopping frequency per day of week and the average shopping frequency per day of week, calculated as below:
[Accumulated shopping frequency per day of week] = [Sum of the shopping occasions for a specific day of week]
[Average shopping frequency per day of week]
= [Accumulated shopping frequency per day Fig.9. Food items distribution HH-1
Fig.10. Food items distribution HH-2
20 of week]/ [number of days of the specific day of week].
Although the total number of shopping events of HH-1 was 97, which is higher than 54 of HH-2, when comparing the average shopping frequency per day of week, the shopping frequency of HH-2 was close to HH-1.
These results can be explained by the different patterns of shopping where HH-1 most often has a single receipt per shopping day that may indicate a purchase in a supermarket, while HH-2 often had multiple receipts for a shopping day and probably shopped in multiple stores.
Due to the twice lower number of shopping days for HH-2, the average shopping frequency per day becomes similar. The peak of shopping frequency of HH-1 is 21 times on Saturdays, while HH-2 is 11 times on Fridays and Saturdays.
Both households had their highest shopping frequency on Saturday. People had more time to shop and to cook food on weekends. In addition, parties usually happen on Friday or Saturday, with guests joining the families for a meal, which also probably attributed on high shopping frequency on Saturday.
High shopping frequency or high number of shopping events have to be complemented with other information in order to make some conclusions on how consumption of goods contributes to environmental impacts. The example could be the transportation methods to the shopping area. For instance, if HH-2 went shopping by foot, which meant HH-2 consumed
no fuel and generated zero environmental impacts
on its way to the shops and then high shopping frequency doesn’t imply a greater environmental impact. Besides, high shopping frequency should not be equal to high products purchasing.
For example, HH-2 might only have purchased one item during one shopping event, while HH-1 bought 100 items during one shopping event. If HH-1 has used a car in order to purchase more items per shopping event, then impact can be greater. Hence, to understand how shopping frequency connects to consumption and waste generation, more factors should be considered, for example, information on the transport mode can be added to the receipt. Another application of the purchasing frequency data can be to investigate whether generation of the avoidable food waste can be related to less frequent but large purchases.
Fig.11. Accumulated and average shopping frequency per day of week of two studied households
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3.3 Distribution of product manufacturing country
Fel! Hittar inte referenskälla. and Fel! Hittar inte referenskälla. respectively display the products origin of HH-1 and HH-2’s. Most of the products consumed by HH-1 were produced in Europe (76%) followed by Asia (5%). Besides, 15% of the products could not be related to a country or region as no such information was provided on the package. A similar product origin was showed by HH-2 with 66% of products manufactured in Europe, 4% in South America and Asia, and 24% are unspecified. Particularly, there were no products consumed by HH-2 from Oceania and Africa during the whole
experimental period.
Several similarities among products origin
between two Swedish households are listed as below:
1. Most of the consumed products were manufactured in Europe, HH-1 76%, 66 % HH-2.
2. Products without specified production countries are the second largest group, HH-1 15% and HH-2 24%
3. Commodities produced in Asia were ranked as third group.
Fig.14 shows the product origin for products manufactured in Europe. It should be mentioned that 57% of the products consumed by HH-1were manufactured in Sweden, while HH-2 had 35%
Fig.12. Products manufacturing distribution HH-1
Fig.13. Products manufacturing distribution HH-2
Fig.14. Manufacturing distribution within Europe
22 of local Swedish manufactured products. The fact that most of the consumed products are imported may be caused by the assortment of goods in the shops, which in turn may depend on the type of the Swedish economy. Sweden is located in the Northern Europe and has cold climate, which constrains development of agricultural industry.
In addition, Sweden is a high technology innovation country, and many of its industrial production are transferred to other countries.
According to the above result, HH-1 consumed 22% more locally manufactured products comparing with HH-2. Provided that transport distance which is assumed to cause the major environmental impact of the consumed products.
HH-1 may have a lower ecological footprint than HH-2. However, as other processes can be more important such as production, the footprint of products has to be assessed before any conclusions of the household footprint through consumption can be made. There is always information missing such as industrial production, transportation information from manufactured country to sales country. For example, the products manufactured in Sweden can be made from some raw material imported from other countries.
3.4 Disposal of recyclables and Biowaste
Tab.10 displays the mass of biowaste and total mass of standardized waste and non-standardized waste in HH-1 and HH-2. In order to standardize the unit, all the data has been unified into kg per capita per year.
According to the results, the mass of biowaste per capita per year was ranked as the heaviest fraction. It constitutes more than half of the total waste for the HH-1 and almost a half for the HH-2. Therefore, decrease in the amount of biowaste can potentially contribute to the waste reduction target (in kg). However, only avoidable biowaste could be reduced, which implied that 54 % of the biowaste could be reduced in the HH-1 and 19% in HH-2 (Tab.11).
Tab.10. Mass of biowaste and total mass of standardized waste and non-standardized waste fractions per capita annually in HH-1 and HH-2
HH_ID OrganicWaste [kg/yr/p]
Glass [kg/yr/p]
Metal [kg/yr/p]
Plastic [kg/yr/p]
Paper [kg/yr/p]
Cardboard [kg/yr/p]
Wood [kg/yr/p]
1 88.21 26.59 2.44 9.10 28.48 12.49 0.25
2 35.65 10.40 0.39 10.99 16.56 9.75 0.05
HH-1: Biowaste > Paper > Glass > Cardboard >
Plastic > Metal > Wood
HH-2: Biowaste > Paper > Plastic > Glass >
Cardboard > Metal > Wood
24 The reasons for generation of the avoidable biowaste can be both eating preferences of the members of the households, and management of the purchased food, for example too many purchases may cause food to spoil before being consumed. Understanding of causes for generation of avoidable biowaste is important for enabling greenhouse gas reduction due to the food production. In Sweden, food stands for almost as large part of the GHG emissions (25%) as the transport and housing (30% each) and more than clothes and shoes (<15%) (Naturvårdverket, 2010).
Tab.11. Mass of biowaste per capita annually
HH_ID
Unavoidable Biowaste
[kg/yr/p]
Avoidable Biowaste [kg/yr/p]
1 40.29 47.94
2 28.71 6.94
According to Tab.12, the households separated considerably more recyclables than the Gothenburg and national average for almost category. This may be due to the higher separation rate during the study, because households collected all the recyclables in the same bag, that may be more convenient and less time consuming then dividing the recyclables into several bags by material type. Such
“single-stream” recycling schemes exist in the USA and UK. However, the extended producer responsibility law in Sweden requires separation of the recyclables according to the type. In particular, both households separated around twice as much per capita plastic waste, which is also the waste that is separated the worst in
Sweden. However, less newspaper waste was collected from the households and considerably less of glass and metal from the HH-2.
According to Tab.10, the second heaviest fraction of solid waste was waste fraction of paper with 28.48 kg generated by HH-1 per capita per year and 16.56 kg produced by HH-2.
The metal and wood fractions were 1 and 2 orders of magnitude smaller than other fractions as they were less applied in packages compared to other materials.
Although metal is commonly used as package of canned food and beverages, products using metal were still less abundant than other materials.
Wood was rarely observed as a package material.
In addition to low amounts used, most of metals are recyclable and wood is biodegradable.
Therefore, metal and wood packages probably cause less environmental impact than other packages from a household perspective.
A comparison can be made to the average sorted package waste per capita and year in Gothenburg and Sweden. Although the statistics from FTIAB are not entirely reliable, the data could still provide some general picture of the recyclable solid waste generation situation in Gothenburg and Sweden. The deviations from the average can be caused by both differences in waste sorting and in consumption patterns. Tab.12 shows the average mass of collected recyclables per capita of Gothenburg and Sweden in 2011, comparing with HH-1 and HH-2.
Tab.12. Average mass collected recyclables in Gothenburg and Sweden per capita annually (FTIAB, 2012)
Waste Fractions Gothenburg, 2011 [kg/yr/p]
Sweden, 2011 [kg/yr/p]
HH-1 [kg/yr/p]
HH-2 [kg/yr/p]
Glass (+ public) 19,55 19,31 26.59 10.40
Paper (with cardboard) 13,34 12,21 19.23 24.05
