The set as developed links three models for enabling the assessment of economic and environmental impacts of waste policy instruments introduced (1) on a macro-economic level for preventing the generation and hazardousness of waste or (2) directly in the waste management sector for shifting waste management activities. The models, their integration into a set and its use are described in the following section.
3.1. The Macro-Economic Model EMEC
The top-down model EMEC (Environmental Medium-Term Economic Model) is a static CGE model of the Swedish economy, initially developed and used for analyzing the interaction between the economy and the environment. The model was evaluated through a number of climate policy analyses conducted in Sweden during the past fifteen years and reported by governmental committees and in peer-reviewed journals [49
]. As part of TOSUWAMA, the model was further developed to include also waste intensities by relating economic activity to the generation of waste in physical quantities [61
]. The waste categories modelled are presented in Table A1
and Table A2
EMEC models 26 industry sectors and 33 composite commodities. Produced goods and services are used together with imports to create composite commodities for domestic use, but can also be exported. Production requires primary factors (two kinds of labor and capital) and inputs of materials, energy and transports (Figure 1
). Households maximize utility subject to an income restriction. Firms maximize profit subject to resource restrictions. Disposal of public services, produced by a single government agent, is subject to a budget constraint. The foreign sector’s import and export activities are governed by an exogenously-given trade balance. Sweden’s products are assumed to have small shares of the total demand on world markets, and therefore, any quantity of exported goods must be sold at given world market prices. The sectors modelled are presented in Table A3
Waste generation is related to the production and consumption of commodities, and thus, economic activity generates firms’ waste by the input used in production and households’ waste by households’ use of outputs. The waste intensities relating waste generation to economic activity were derived from Swedish national waste statistics for the year 2008 [50
]. Since firms are assumed to be cost minimizing in the choice of labor, capital, energy, transports and materials for producing outputs, the substitutions between these inputs and productivity changes in the use of the inputs affect waste generation. Households’ waste generation is affected by the consumption of goods and services. Waste management is not represented as a single sector, but its activities are split between the sectors “Water and sewage” and “Services”.
3.2. The Waste Management Model NatWaste
The bottom-up model, NatWaste (National Waste management), is a systems engineering model for strategic planning of national waste management systems [40
]. It has been used to assess, for instance, the introduction of a landfill tax and landfilling bans in Sweden (ibid.) and goals for biological treatment and material recycling [64
Based on cost optimization, NatWaste calculates the cost-effective mix of waste management processes (Figure 2
) for a given quantity of waste. The cost-effective mix is the set of processes that gives the lowest total net economic costs, excluding external environmental costs and private consumer time, with respect to the conditions defined. These include the collection, transportation and treatment options available for each included waste category (Table A4
). Costs include variable costs for collection, local and regional transportation and treatment of waste, auxiliary energy and materials, annualized investment costs for new or expanded process capacity and revenues for recovered material and energy. Capital costs for investments already made are not included, but treated as sunk costs. Results from NatWaste specify the cost-effective mix of waste management processes, in terms of waste quantities processed and resources recovered by each process
NatWaste models waste categories of a certain quantity and composition from industry and households from the point where it is collected from the first generator (Figure 2
). Waste is then treated in the system, usually in a sequence of different processes. Resources exit the system exit as recovered materials (e.g., scrap metal, glass, different plastics), fertilizer (compost and digestate) and energy (electrical power, heat and biogas) to be absorbed by the markets. Auxiliary electricity and vehicle fuels are supplied for running the system.
Swedish waste management is modelled as a number of coupled generalized municipal waste management systems [40
]. Each generalized system reflects different characteristics, such as the size of the municipality and access to a district heating system. Every Swedish municipality is allocated to one such generalized system, so that the sum of generalized municipal systems represents the national system. In total, 49 waste categories from industry and households are modelled (Table A1
and Table A3
), including all non-hazardous waste categories generated in Sweden, except waste from the mining industry, waste considered as biofuel and other wastes that were considered not to be affected by the policy instruments to be assessed in TOSUWAMA.
3.3. The LCA Waste Management Model SWEA
The bottom-up model SWEA was developed for life cycle assessments of potential environmental impacts of waste management and waste prevention in Sweden [65
]. It builds on previous work [36
], utilizing Life Cycle Inventory (LCI) data, in which processes are represented by inputs and outputs of material and energy flows of unit processes, from existing models, databases and earlier studies of waste management processes, such as collection, treatment and recycling of waste.
SWEA can be represented as consisting of a foreground system, corresponding to the WMS in NatWaste, and a background system. The foreground system includes collection, transportation, separation and treatment of the different waste categories, while the background system includes process models of the production of fuels, electricity and materials used by the foreground system (Figure 3
). In line with established LCA methodology for waste management [66
], credit is given to the recovery of resources from waste in the foreground system through system expansion and modelling of the avoided processes. In comparative assessments of scenarios where waste quantities remain constant, waste generation is excluded from the model. In analyses of waste prevention measures, the avoided burden of manufacturing and the use of products corresponding to the reduced waste amounts are calculated.
SWEA includes the same 49 waste categories from industry and households and waste process options as NatWaste (Table A1
, Table A3
and Table A4
). LCI data for thermal and biological waste treatment, landfill disposal and recycling of inert waste was generated by the ORWARE (Organic Waste Research) model [68
]. Additional LCI data on recycling were provided from Swedish datasets [65
] or EcoInvent. LCI data on the background system and avoided processes (such as materials from virgin resources, energy supply, vehicle fuels and chemical fertilizer) were taken from the database EcoInvent 2.0 (Ecoinvent Centre, Dübendrof, Switzerland) [69
]. The potential environmental impacts of emissions to and resources from the environment can be assessed by using Life Cycle Impact Assessment (LCIA) methods, such as the ReCiPe method [70
] (Table A5
3.4. Linking of the Models
The analysis starts by establishing a base year solution in both EMEC and NatWaste, using official statistics on the economy and waste (in TOSUWAMA, the year 2006). Then, scenario assumptions for exogenous variables in EMEC (Figure 4
) are used for solving the model´s endogenous variables for a future year (in TOSUWAMA, the year 2030). The solution for endogenous variables, such as the waste quantities generated (Table A1
and Table A2
) in included sectors (Table A3
) and the price changes of raw materials, energy, transport and labor used or produced in waste management compared to the base year are transferred to NatWaste (Figure 4
). NatWaste then calculates the cost-effective process mix for managing the new waste amounts and the resulting marginal costs for each waste category and sector (Table A1
and Table A3
). The marginal costs are transferred back to EMEC, in which they are handled as prices of waste management services (Figure 4
), so that EMEC can calculate how firms and households react to the price change of waste management services. After the iterations between EMEC and NatWaste, described in more detail in [61
], the cost-effective process mix for waste management and waste amounts are transferred from NatWaste to SWEA (Figure 4
). This means that the results from NatWaste determine what waste category amounts will be handled by what process models in SWEA. Potential environmental impact is then calculated based on the resulting resource use and emissions calculated by SWEA (Figure 4
and Table A5
). Note that while physical flows (material and energy) within, to and from the waste management system are the basis for modelling in NatWaste and SWEA, EMEC models economic relations in the Swedish economy, including all industrial sectors, the public sector and households.
The primary results from the linked models are: (1) the waste amounts generated by category and sector (Table A1
, Table A2
and Table A3
); (2) the cost-effective process mix for managing this waste (Table A4
); and (3) its potential environmental impacts (Table A5
). It is also possible to obtain results for recovered resources in terms of energy recovery (amounts of electricity, heat and biogas), material recycling (amounts of recycled materials) and biological treatment (amount of organic fertilizer). In addition, structural changes in the Swedish economy can be obtained.
Data transfer between the three models is achieved by means of a “soft link” set up, meaning that numerical data are transferred manually between the models. No manual translation of data is required, since the models are harmonized in terms of parameter terminology for waste categories, generating sectors and management processes. NatWaste and SWEA share the scope and level of detail in the representation of waste categories and technical waste management options, and both use physical flows (material and energy) as a basis for modelling. Where NatWaste considers costs and revenues linked to processing physical flows to, from and within waste management, SWEA considers how the same processing of physical flows links up to generated or avoided environmental impacts. EMEC models economic relations in the Swedish economy, including all industrial sectors, the public sector and households. The waste categories modelled in EMEC correspond to the European Waste Catalogue for Statistics(EWC-Stat) waste categories in the European Waste Statistics Directive [71
]. Many of these categories, however, are too aggregated to be used for modelling impacts at the process level performed with NatWaste and SWEA. Therefore, most waste categories were disaggregated further in several material- or product-based subcategories when modelled in NatWaste and SWEA [61
] (Table A1
After establishing a common reference solution for the chosen scenario year, the assessment of policy instruments is performed by introducing them in EMEC, if for waste prevention, or in NatWaste, if for waste management (Figure 4
). The same iterative and sequential procedure as described above is performed, and finally, the solutions are compared to that of the reference solution. This procedure can be performed under varying future external scenarios, which are described in some detail in the following section. As an illustration, the assessment of the policy instrument Differentiated value-added tax (VAT) is also briefly described.
3.5. Assessment of Policy Instruments in Scenarios
The main purpose of developing the set of linked models was to assess waste policy instruments in TOSUWAMA. Policy instruments were chosen and detailed during the course of the program in parallel with the model development [72
]. First, policy instruments were suggested by stakeholders or identified in the literature. Then, 14 instruments were chosen as interesting candidates for assessment and described in some detail (Table 1
). The main goals of the instruments differ: 1–8 direct waste management in a more sustainable direction; 9–13 prevent the generation and hazardousness of waste; and 14 combines both goals. The set of linked models is primarily suited for analyzing easily-quantifiable policy instruments, and after a first examination, Instruments 1–3 and 9–11 were chosen for assessment with the set of linked models. Although suitable for the set of linked models, Instruments 4 and 5 were instead assessed by literature studies, while Instrument 12 could not be assessed because of a lack of data (see Section 4.5
). Other assessments in TOSUWAMA dealt with instruments 3, 6–8, 13 and 14 [14
To explore the robustness of policy instruments, assessments were made with reference to different future developments, illustrated in scenarios for the year 2030 [62
]. Besides a reference scenario assuming developments in accordance with official forecasts made in 2008, four scenarios were set up (Table 2
The scenarios, designed to explore possible, but very different future developments of society, were initially qualitatively described. For assessment with the set of linked models, scenarios were translated into quantifications, where key assumptions concerned Gross Domestic Product (GDP), world trade, primary product prices, oil prices, employment, carbon permit price, waste intensities and energy system performance (Figure 4
). The scenarios were implemented in EMEC and then transferred to NatWaste through the relative price changes calculated by EMEC. The price changes in each scenario were used for recalculating the level of unit costs and revenues for waste management in NatWaste, such as treatment and transportation costs and material and energy revenues. This ensured a consistent implementation of scenarios between the two models. SWEA implemented the scenarios in two ways, both through results on waste generation transferred via NatWaste, and assumptions on heat and electricity mixes in the energy system.
To illustrate the use of the set of linked models, the assessment of the policy instrument Differentiated VAT is briefly described [14
]. A reduction of the VAT on households’ service consumption (excluding transportation), from current levels of 25% or 12% to 6%, could shift consumption from goods to services and thereby reduce waste generation and environmental impacts. The assessment of one of the two studied alternatives to finance such a tax cut, by decreasing government transfers to households, is presented.
As a first step, the EMEC model calculated an increase in service consumption by 3.6%, compared to a case without the policy instrument. It was observed that while consumption of goods fell, households’ total consumption expenditures were almost unchanged. Investments fell marginally as did imports and exports. GDP decreased by 0.1%. As a result, there was also a decrease in households’ waste generation by about 1% (corresponding to 125 ktons).
The waste amounts generated (per category and sector) for the cases with and without the policy instrument were transferred from EMEC to NatWaste. Based on this, NatWaste calculated the cost-effective process mix for managing the waste. The introduction of the Differentiated VAT only affected the amount of generated waste, but not the waste management system itself (in terms of costs, revenues or performance parameters). Thus, results only differed between the two cases in terms of waste amounts processed, while the optimal mix of waste management processes remained the same.
Finally, the waste amounts and optimal mix of waste management processes were transferred from NatWaste to SWEA. Based on this, SWEA calculated potential environmental impacts from waste management with and without the policy instrument (Table 3
). “Waste management system” impacts are in the majority of categories negative since avoided burdens resulting from, e.g., recycled materials and recovered energy, are larger than direct impacts (see Section 3.3
For most impact categories, the net result from waste management was hardly affected by the policy instrument. This can be explained by the fact that the indirect avoided burdens also decreased when waste amounts decreased. SWEA also calculated reduced impacts from production resulting from waste prevention, “avoided material production”, corresponding to the 1% reduction of households’ waste generation. When taken into account, net potential environmental impacts were reduced by up to 7% with the policy instrument. Marine eutrophication was an exception because of a relatively small contribution to this impact category from waste management itself, but a heavy dependence on emission from beef production, which was reduced as a result of the waste prevention calculated by EMEC. The results were similar in all scenarios studied (Table 2
It was concluded that the studied alternative of Differentiated VAT may reduce environmental impacts without significantly impacting the economy as a whole. However, the relatively limited reduction of environmental impacts points to larger differentiation of VAT levels or combinations with other instruments for incentivizing larger reductions. Such options were however not assessed within TOSUWAMA.