Model for the assessment of future plastic waste amounts

Transcription

1 Plastic ZERO - Public Private Cooperations for Avoiding Plastic as a Waste Model for the assessment of future plastic waste amounts Sweden and Malmo April 2014 City of Malmo City of Malmö City of Hamburg SIA Liepajas RAS Malmo Regional Solid Waste Management Ltd. I/S Amager Ressourcecenter Aalborg University LIFE10 ENV/DK/098 - with the contribution of the LIFE financial instrument of the European Union 1

2 Table of contents 1 Introduction The basic idea Model structure Forecasting Scaling Scenarios The baseline scenario Highoil scenario Lowoil scenario Quality Assurance Done by Poul Pedersen Date Quality assured by Frits Møller Andersen, Massimo Pizzol Approved by Mette Skovgaard

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4 1 INTRODUCTION The model described in this paper is intended as a tool for assessment of annual waste amounts on national and local levels based on available waste data combined with certain economic forecasting parameters. It contains a national and a local element plus a description of how waste amounts could ideally be calculated if all data and information needed in theory were available in practice. The model is devised to be able to use data from official sources like Eurostat and national statistical bureaus. A number of restrictions apply as to the practical use of the model: - statistics especially waste statistics are scarce, - identifying products and product types made of or containing plastic in the official statistics is difficult, - a reliable foundation for establishing waste quotients and waste profiles as described in the next section of this paper for a huge and ever increasing number of plastic products on the market does not exist. The basic line of thought presented in this paper can be used to assess waste amounts for any type of product, material or waste category like for example plastic, glass, paper, cardboard, textiles, metal, food etc. The forecasting part of the model, wastemodel_sweden.xls, and the scenario part, Scenarios Sweden.xls, are available as spreadsheets in the Road map section on the Plastic Zero website, 2 THE BASIC IDEA This section contains a description of how waste amounts ideally can be assessed. The specific calculations in this project are carried out in a different and simpler way due to the limited quality of available data. These are described in the final section of this document. The mathematical presentations in this section are made under consultancy by Professor Mogens Steffensen, University of Malmo, Department of Mathematical Sciences. The description below falls in two parts, packaging and non-packaging, the latter covering all products of a years supply not being packaging. As it can be seen the packaging part is easier to handle than the non-packaging part because it can be assumed that annual waste equals annual supply. Yearly waste from packaging is assumed to equal yearly supply based on the assumption that packaging turns to waste the same year it is consumed. Thus, 4

5 where and stand for packaging waste and packaging supply, respectively. Since supply and hence waste varies from one year to another packaging waste in year n is: where and denote packaging waste and packaging supply in year n. Non-packaging This category covers [the plastic content of]: Building/Construction (bc) Automotive (au) Electric and Electronic Equipment (ee) Housewares (hw) Agriculture (ag) Others (furniture, textiles etc.) (ot) For each of these categories waste amounts depend on previous years supply according to the waste profile of each category/product. The waste profile shows how supply in a specific year turns to waste over the products lifetime as the sum of waste quotients of the years. The waste quotient - a number between 0 and 1 - is the share of a years supply that turns to waste in a certain year e.g. 0.0 in the first year, 0.1 in the second year, 0.25 in the third year etc. until the original supply is gone. From the point of view of the year n ie the year for which waste amounts are to be calculated it is evidently crucial how much of previous years supplies turn into waste in year n. This is shown below. The total amount of waste from a specific application a in the year n can be expressed: is waste from the specific application a (the abovementioned Building/Construction, Automotive etc.) in year n, is the proportion of supply for an application a from a given year that turns into waste after j years, is the supply of application a from year n-j, is the amount of years it takes (after the year of supply) until the supply from application a has fully turned into waste. The lifetime including the year of supply where the proportion turns into waste, is. See footnote 1 The waste quotients are the shares of a years supply that turn to waste in each year during its lifetime. In the example in the footnote it would be 10 per cent, 10 per cent, 20 per cent, 20 per cent, 30 per cent and 10 per cent in a six year lifetime. 1 Assume that the supply of an application a with a lifetime of six years including the year of supply ( ) amounts to tons in year, tons in, in, in, in and in. With a waste profile of this would give rise to tons of waste in year n, of which tons (50.000*0.1) from the year, tons (52.000*0.3) from, tons (54.000*0.2) from, tons (55.000*0.2) from, tons (57.000*0.1) from and tons (60.000*0.1) from. 5

6 The waste profile of an application consists of its waste quotients in each year of its lifetime: The fact that the full supply from a given year has turned into waste within years can be written as The total amount of non-packaging waste in the year n can be expressed: where a stands for [the plastic content in] the applications: A = {bc, au, ee, hw, ag, ot}. The total waste amount from packaging and non-packaging in the year n can eventually be expressed: Note that in the formulas above the packaging category could have been accounted for as a special application for which and. Then the total waste would have been obtained by summing over all applications. 3 MODEL STRUCTURE In practice the model consists of two modules linked together to make changes in one appear as changes in the other. The modules are 1. Calculation of national waste amounts 2. Calculation of local waste amounts Module 1 Calculation of national waste amounts This module contains: - Waste data for 2010 in the waste categories Building/Construction Automotive Electric and Electronic Equipment (WEEE) Housewares Agriculture Others (furniture, textiles etc.), - The economic forecasting parameters for each waste category, - Calculated waste amounts for the period , Module 2 Calculation of local waste amounts 6

7 This module contains: - National waste amounts from module 1, - Economic indicators for downscaling (Gross Value Added) at national and local level. GVA at the local level has not been available in the statistics. An approximation based on local share of total workplaces in 2011 and expected population growth in has been used instead. - Expected population growth at national and local level, - Calculated waste amounts at local level. 4 FORECASTING Forecasting of plastics waste for the period is conducted in the model on a national level without, however, forgetting that the final objective is to be able to calculate waste amounts at local level i.e. for Malmo in the forecasting period. See the model, wastemodel_sweden.xls. Conversion from national to local level is done by downscaling as described in more detail below. Since waste data are only available for a single year forecasting is based on so called fixed coefficients. A fixed coefficient is a locked relation between an independent and a dependent variable like e.g. GDP and plastic demand. In the model the fixed coefficients apply to the relations between the waste categories and the economic indicators as described in the previous section. The economic indicators are (in italics): Household packaging waste - Final Consumption; Business packaging waste Gross Value Added; Building and Construction waste Gross Value Added in construction; Automotive - Number of new registered passenger cars (ten years time lag); WEEE (waste from electric and electronic equipment) - Final Consumption; Housewares - Final Consumption; Agriculture Gross Value Added in agriculture; Other (furniture, textiles etc.) - Final Consumption. These time series are inserted into the model and form the basis for the forecasting in each waste category. See the model, sheet economic indicators. 5 SCALING Forecasting is done on the national level, whereas the final goal as mentioned is to be able to predict waste amounts at a local level i.e. in Malmo in the project period. A conversion from national amounts to local amounts hence must take place. The most simple conversion factor would be the size of the population in Sweden 7

8 compared to that of Malmo. Using the relative size of population for scaling, however, would evidently be misleading on account of differences in degrees of occupation, composition of industries and economic activity. Downscaling can be done using one or more of the below mentioned indicators: Consumption of selected goods and services, Disposable incomes, Number of households, Type of housing, Population, Occupation, Composition of business sectors and industries, Economic activity, Other. The scaling factor plays an important part for the calculated waste amounts at the local level. There is no way of knowing exactly how Malmo differs from the rest of Sweden when it comes to plastic waste. We have chosen to apply general economic activity (Gross Value Added (GVA)) or rather the ratio between GVA on the national level and GVA at the local level for scaling factor. Since attempts to retrieve data for GVA at local level have come out unsuccessful an approximation based on the local share of workplaces of total workplaces in Sweden for 2011 has been used instead. This approximation to GVA in Malmo City is forecast along the same lines as the whole country only adjusted for assumed differences in population growth between Sweden and Malmo City. See the model, sheet Malmo. 6 SCENARIOS Scenarios are contemplations over the future with a focus on a specific issue in this case plastic waste. Scenarios can reflect assumptions about complex phenomena like patterns of production and consumption, political initiatives and legislation, developments in technology or less complex phenomena like oil prices and freight rates. The two scenarios below are based exclusively on assumptions about oil price developments and their implications for the generation of plastic waste. One scenario is based on rising oil prices and the other on falling oil prices. Accordingly the forecasting procedure has two sides: a baseline scenario following a macroeconomic prediction on one hand and two scenarios based on different paths of oil price developments in the future on the other. 7 THE BASELINE SCENARIO National level 8

9 Based on the following waste amounts for the year 2010 at national level (tons): households: business: : Building/Construction: Automotive: Electric and Electronic Equipment (WEEE): Housewares: Agriculture: Others (furniture, textiles etc.): Non-packaging: Total: and the above mentioned forecasting parameters the baseline scenario at national level looks like this: tons Total plastic waste Sweden Non-packaging representing a growth from tons in 2013 to tons in 2025 or an increase of one hundred thousand tons over the period equalling an annual growth rate of 2.5%. waste accounting for 60 percent of the total is expected to grow from tons in 2013 to tons in 2025 whereas non-packaging waste is forecast to go from tons to tons in the same period. The following figure shows calculated waste amounts for each of the waste categories: 9

10 tons Plastic waste fractions Sweden households business Building/con struction Automotive WEEE House wares Agriculture Others (furniture etc) The figure shows that packaging waste amounts from households is expected to increase from tons in 2013 to almost tons in 2025 or close to 35% in the period still a little less than packaging waste from the business sector. Local level Based on these waste amounts for the year 2013 at local level (tons): households: business: : Building/Construction: 566 Automotive: 540 Electric and Electronic Equipment (WEEE): 807 Housewares: 591 Agriculture: 479 Others (furniture, textiles etc.): Non-packaging: Total: and the above mentioned forecasting parameters the baseline scenario at local level looks like this: 10

11 tons Total plastic waste Malmo waste Non-packaging waste representing a growth from ca tons in 2013 to tons in 2025 or an increase of tons over the period equalling an annual growth rate of 3.7%. The projected growth of waste amounts in Malmo is significantly higher than the equivalent growth in the whole country. The following figure shows calculated waste amounts for each of the waste categories: tons Plastic waste fractions Malmo households business Building/constr uction WEEE Automotive House wares 5000 Agriculture Others (furniture etc) The figure shows a comparable development in packaging waste amounts from households and businesses respectively 48% and 56% over the period. 11

12 8 HIGHOIL SCENARIO Assume an oil price development following a steep upward demand curve in emerging economies combined with increased political and military unrest in a number of oil supplying regions. Let this result in an oil price that goes up by 20% a year between 2013 and 2020 whereafter it flattens out for the rest of the period. Crude oil price in 2013 = USD100 per barrel USD/bbl : : : : : : : : : : : : : 365 Generation of plastic waste would be affected in a general as well as a specific manner by a trend in oil prices like the one shown above: - An overall contraction in economic activity would lead to a reduced demand for plastics and a subsequent reduced generation of plastic waste 3, - A price hike in oil would lead to a relative rise in prices in raw materials for plastic manufacturing which in turn would bring about a weakening of competitive capacity and eventually a decline in demand and waste. Combine this with assumed price demand elasticities for the waste fractions as shown below. Real life elasticities are hard to predict. They depend to a large extent on the replaceability of a material or a product with a competing material/product. households, high business, high Building/construction, low Automotive, low WEEE, medium House wares, medium 2 An equivalent although not identical growth in real terms oil prices took place between 2002 and The correlation between changes in oil prices and general economic activity depends on the unique (obviously highly complex) historical context to be investigated. For example: oil prices rose steadily in the 00 s accompanied by a likewise steady growth in economic output, while opposite directions prevail in other periods of history. For simplicity a negative correlation between oil prices and overall economic activity is preconditioned in both scenarios in this project. 12

13 Agriculture, low Others (furniture etc), medium. For exact definitions, figures etc. see the model oil_swed_malm.xls. to obtain a waste generation in Sweden as shown in the following graph as a consequence of a steep upward oil price curve. Sweden, highoil scenario tons Highoil Plastic waste fractions Sweden households business Building/construct ion Automotive WEEE House wares Agriculture Others (furniture etc) This development covers as can be seen in the model a fairly straightline waste effect from reduced overall economic growth and a substantial decline following reduced demand for plastic products. Malmo, highoil scenario For Malmo with the same assumptions as for the nation and same share of total waste for the individual waste fractions as in the baseline scenario for Malmo: 13

14 tons Plastic waste fractions Malmo households business Building/constru ction WEEE Automotive House wares Agriculture Others (furniture etc) Waste amounts tend to fall over the whole period at approximately the same rate as for Sweden as a whole. 9 LOWOIL SCENARIO Imagine a scenario with new vast discoveries of shale and tar sand oil and a concurrent massive cost reduction in non fossil energy technologies. Under such circumstances let the oil price go down 20% per year until 2020 and remain on a level of USD15-20/bbl for the rest of the period: Crude oil price in 2013 = USD100 per barrel USD/bbl 2013: : : : : : : : : : : : : 15 Generation of plastic waste would be affected in a general as well as a specific manner by a trend in oil prices like the one above: - An overall expansion in economic activity would lead to an increased demand for plastics and a subsequent increased generation of plastic waste, see footnote 4, 14

15 - A price dive in oil would lead to a relative decline in prices in raw materials for plastic manufacturing which in turn would lead to a stronger competitive ability and eventually an increase in demand and waste. Elasticities in this case express the increase in demand following falling prices. households business Building/construction Automotive WEEE House wares Agriculture Others (furniture etc) high high low low medium medium low medium For exact definitions, figures etc. see the model oil_swed_malm.xls. Sweden, lowoil scenario The massive decline in oil prices could lead to a growth in waste amounts in Sweden as shown in the graph below. tons Lowoil Plastic waste fractions Sweden households business Building/constr uction Automotive WEEE House wares Agriculture Others (furniture etc) This scenario implies a doubling of plastic waste in the period. The increase in waste amounts is much higher than the decline in waste in the highoil scenario. 15

16 Malmo, lowoil scenario For Malmo with the same assumptions as for the nation and same share of total waste for the individual waste fractions as in the baseline scenario for Malmo: tons Plastic waste fractions Malmo households business Building/constru ction WEEE Automotive House wares Agriculture Others (furniture etc) Waste generation in this scenario is growing higher than in the equivalent scenario for Sweden. 16